research paper inflation

Understanding US inflation during the COVID-19 era

• Download final paper with comments, discussion summary and online appendix
• Download data/programs for main paper
• Download data/programs for Furman comment
• Download data/programs for Şahin comment

Subscribe to the Economic Studies Bulletin

Laurence ball , laurence ball professor of economics - johns hopkins university daniel leigh , and daniel leigh division chief, world economic studies division - imf research department prachi mishra prachi mishra chief of the systemic issues division, research department - international monetary fund.

September 7, 2022

The paper summarized here is part of the Fall 2022 edition of the Brookings Papers on Economic Activity (BPEA) , the leading conference series and journal in economics for timely, cutting-edge research about real-world policy issues. The conference draft of this paper was presented at the Fall 2022 BPEA conference . The final version was published in the Fall 2022 issue by Johns Hopkins University Press.

See the Fall 2022 BPEA event page to watch conference recordings and read conference drafts of all the papers from this edition.  Submit a proposal to present at a future BPEA conference  here .

Read final paper with comments, discussion summary and online appendix here»

Download data/programs for main paper here»

Download data/programs for Furman comment here»

Download data/programs for Şahin comment here»

The Federal Reserve likely will need to push unemployment far higher than its 4.1 percent projection if it is to succeed in bringing inflation down to its 2 percent target by the end of 2024, suggests a paper discussed at the Brookings Papers on Economic Activity (BPEA) conference on September 8.

The paper, Understanding US Inflation During the COVID-19 Era , analyzes why the pandemic-related surge in inflation has persisted and runs simulations under different assumptions to look at where inflation might be heading.

According to the authors—Laurence Ball of Johns Hopkins University and Daniel Leigh and Prachi Mishra of the International Monetary Fund—it is unlikely, but not impossible, for the Fed to achieve the soft landing (substantially lower inflation with only modestly higher unemployment) that it projected in June.

The median Fed policymaker projected 4.1 percent unemployment at the end of 2024, up only modestly from 3.7 percent in July. The median projection for inflation, as measured by the Commerce Department’s personal consumption expenditures (PCE) price index, was 2.2 percent at the end of 2024, down from a four-decade high of 6.8 percent in June. The PCE Index eased modestly in July (prices were up 6.3 percent from a year earlier). The Labor Department’s more widely known consumer price index (CPI) also hit a 40-year high in June (9.1 percent) before easing slightly to 8.5 percent in July.

“If either the labor market doesn’t behave, or expectations don’t behave, the small increase in unemployment the Fed projects won’t be enough.”

So far this year the Fed has raised its short-term interest rate target by 2.25 percentage points, from near zero, and projected in June that to tame inflation it would need to raise the target by only an additional percentage point this year and a half percentage point next year.

Fed policymakers, as well as most economists (including the paper’s authors), had expected that the upturn in inflation that began in March 2021 would prove transitory. The paper cited three reasons why those expectations proved to be wrong: first, unforeseeable events such as Russia’s invasion of Ukraine and the persistence of pandemic supply-chain disruptions; second, failure to account for the pass-through of specific price shocks (such as to energy and auto prices) into the core, or underlying, rate of inflation; and, third a focus on the unemployment rate (which has only recently fallen back to pre-pandemic levels) as an indicator of labor market tightness rather than the very high ratio of job vacancies to unemployed workers (V/U).

The very high V/U ratio in 2021 and this year can explain three-quarters of the rise in monthly core CPI inflation as measured by a Federal Reserve Bank of Cleveland index that strips out the effects of unusually large price changes in certain industries, according to the paper. Some of the consumer demand that fueled the economy, as well as the labor market tightness, in turn, can be explained by the Biden administration’s $1.9 trillion American Rescue Plan enacted in March 2021. Without it, the authors estimate that annualized monthly core inflation would have been 3.7 percent in July rather than 6.5 percent.

According to the paper, whether the Fed can achieve its objectives depends on whether it is possible to slow demand in such a way that vacancies decrease but unemployment doesn’t rise (returning the V/U ratio to its pre-pandemic norm) and on whether consumers and businesses start to expect that high inflation will continue for the longer term, and thus plan for it. Under optimistic assumptions for both the V/U ratio and long-term inflation expectations (and assuming the Fed’s 4.1 percent unemployment projection proves correct), the paper projects the Fed will bring core inflation down close to its target by the end of 2024. However, under the most pessimistic assumptions for both the V/U ratio and inflation expectations, core inflation rises to about 8.8 percent if unemployment moves up only to 4.1 percent.

“If you make quite-optimistic assumptions, we might get something close to what the Fed expects,” Ball said in an interview with The Brookings Institution. “But if either the labor market doesn’t behave, or expectations don’t behave, the small increase in unemployment the Fed projects won’t be enough. Either inflation will stay substantially higher, or we will have higher unemployment and a substantial economic slowdown.”

Ball, Laurence, Daniel Leigh, and Prachi Mishra. 2022. “Understanding US Inflation during the COVID-19 Era.” Brookings Papers on Economic Activity , Fall. 1-54.

Furman, Jason. 2022. “Comment on ‘Understanding US Inflation during the COVID-19 Era’.” Brookings Papers on Economic Activity , Fall. 55-65.

Şahin, Ayşegül. 2022. “Comment on ‘Understanding US Inflation during the COVID-19 Era’.” Brookings Papers on Economic Activity , Fall. 65-76.

Discussants

Daniel Leigh and Prachi Mishra are employees of the International Monetary Fund, which conducts a review of all externally published pieces. The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. The authors are not currently an officer, director, or board member of any organization with a financial or political interest in this article.

David Skidmore authored the summary language for this paper. Becca Portman assisted with data visualization.

Economic Indicators Federal Reserve

Economic Studies

Brookings Papers on Economic Activity

David Wessel

August 21, 2024

Aaron Klein

July 29, 2024

Louise Sheiner

January 12, 2024

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 29 April 2024

Investor attention and consumer price index inflation rate: Evidence from the United States

• Panpan Zhu 1 ,
• Qingjie Zhou 1 &
• Yinpeng Zhang 2  

Humanities and Social Sciences Communications volume  11 , Article number:  541 ( 2024 ) Cite this article

739 Accesses

Metrics details

Explaining and forecasting inflation are important and challenging tasks because inflation is one focus of macroeconomics. This paper introduces novel investor attention to the field of inflation for the first time. Specifically, the Granger causality test, vector autoregression (VAR) model, certain linear models, and several statistical indicators are adopted to illustrate the roles of investor attention in explaining and forecasting inflation. The empirical results can be summarized as follows. First, investor attention is the Granger cause of the inflation rate and has a negative impact on inflation. Second, predictive models that incorporate investor attention can significantly outperform the commonly used benchmark models in inflation forecasting for both short and long horizons. Third, the robustness checks show that updating investor attention or the model specification does not change the conclusion of the crucial role of investor attention in explaining and forecasting inflation. Finally, this paper proves that investor attention influences inflation through inflation expectations. In summary, this paper demonstrates the importance of investor attention for macroeconomics, as investor attention affects inflation.

Similar content being viewed by others

Consequences of firm-specific stock price crashes on analyst forecasts: Evidence from China

Cyclical dynamics and co-movement of business, credit, and investment cycles: empirical evidence from India

Asset pricing and nominal price illusion in China

Introduction.

Inflation reflects the rise in price level in a society for a certain period. A lower inflation level is detrimental to the growth of social wealth, while a higher inflation level leads to a decrease in social wealth, and a given society needs a moderate level of inflation to promote sustained wealth growth. Thus, inflation stands as one focus in academia because of its diverse economic and financial aspects and has become one of the most important goals of macroeconomic regulation in various countries (Woodford and Walsh, 2005 ; Yellen, 2017 ; Effah Nyamekye and Adusei Poku, 2017 ). Recently, the urgency of research on inflation has further strengthened and has received increasing attention from central banks due to multiple factors, such as COVID-19, the excessive issuance of global currency, carbon neutrality expectations, and variations in international crude oil prices.

The explanation and forecasting of inflation are two attractive aspects of research on inflation and have made numerous achievements. For example, Fama ( 1975 ) and Zakaria et al. ( 2021 ) highlight the importance of understanding inflation with traditional economic and financial factors, i.e., interest rates and crude oil; McKnight et al. ( 2020 ) and Choi ( 2021 ) adopt sophisticated models to forecast actual inflation and argue for the suitability of the selected models in inflation forecasting. However, the shortcomings of current research are also evident. For example, current research overly emphasizes the role of traditional economic and financial factors in inflation determination and lacks in-depth investigations on the roles of emerging factors (Aparicio and Bertolotto, 2020 ). Additionally, the commonly adopted New Keynes Phillips Curve (NKPC)-based models for inflation forecasting have reached a limit, and their forecast accuracy is weak (Mavroeidis et al. 2014 , Aparicio and Bertolotto, 2020 ).

Thus, discovering novel factors to explain and accurately forecast inflation has become an interesting and important issue. However, relevant investigations seem to be limited, which represents a research gap and motivates the authors to identify and explore novel factors and help fill the gap.

Investor attention, generated from behavioural finance, is an emerging issue in current research on finance and economics and is theoretically connected with inflation. First, investor attention affects asset pricing in diverse ways (Chen et al. 2022 ; Cai et al. 2022 ; Liu et al. 2022 ), and inflation is a direct reflection of general price variations in a society. Second, investor attention reflects individuals’ information acquisition (Chen and Lo, 2019 ), and information is one of the most critical factors in forming inflation expectations (Larsen et al. 2021 ). According to the theory of NKPC (Friedman, 1968 ; Chen and Lo, 2019 ), inflation expectations affect inflation. Therefore, investor attention and inflation are connected.

Despite the theoretical connections, few studies discuss the connections between investor attention and inflation, making this paper of significance to research both inflation and behavioural finance. In this context, the research objective of this paper is to explore whether investor attention can empirically affect inflation. Consequently, this paper aims to combine behavioural finance and macroeconomics to explore what kind of relationship is hidden between the two issues. Based on the current investigations of explaining and forecasting inflation, the theoretical connection between investor attention and inflation, the research question, and the research objective, this paper proposes three hypotheses:

H1: Investor attention can empirically explain inflation.

H2: Investor attention can empirically forecast inflation.

H3: Investor attention affects inflation through its influence on inflation expectations.

The originality of this paper lies in its connection of investor attention and inflation, thus providing new empirical insight into macro inflation with respect to micro-investor attention. To the best of our knowledge, this paper makes the following contributions to the literature regarding investor attention and inflation. First, this paper may represent the first attempt to empirically explain and forecast inflation from the aspect of investor attention, thus extending the application of emerging investor attention to conventional macroeconomics. Second, this paper provides additional empirical evidence that inflation is influenced by not only conventional factors but also emerging factors, for example, investor attention. The empirical process can be summarized as follows. First, this paper constructs a VAR model and implements the corresponding Granger causality test to explore the explanatory power of investor attention on inflation. Second, based on the models for explaining inflation with investor attention, we construct predictive models to further compare and evaluate the forecast accuracy with commonly used techniques in inflation forecasting during short and long horizons. Third, this paper updates the data and model specifications to implement robustness checks to ensure the rigor required of an academic research paper. Finally, given the connections between investor attention and inflation expectations, this study empirically tests the correlation between inflation expectations and investor attention.

This study is structured as follows. Section 2 provides a brief literature review on inflation and investor attention. Sections 3 and 4 present the data and methodologies, respectively. The empirical results are shown in Section 5. Section 6 describes the robustness checks. Section 7 further discusses investor attention and inflation expectations. Section 8 concludes the paper.

Literature review

This paper focuses on inflation from two perspectives, i.e., inflation explanation and forecasting; in fact, numerous relevant investigations have been performed. For example, Canova and Ferroni ( 2012 ) attribute variations in inflation to monetary authorities. Murphy ( 2014 ) and Friedrich ( 2016 ) explain inflation by the Phillips curve and its derivative models. Hakkio ( 2009 ), Monacelli and Sala ( 2009 ), Ciccarelli and Mojon ( 2010 ), and Mumtaz and Surico ( 2012 ) argue that several factors can influence inflation, such as industrial production, unemployment rates, and nominal wages. Altissimo et al. ( 2009 ) investigate inflation from the perspective of the aggregation mechanism, and Forbes et al. ( 2021 ) use a ‘trendy’ approach to understand inflation dynamics. More recently, Bouras et al. ( 2023 ) and Liu and Ma ( 2023 ) attribute inflation to the cost of firms and the exchange rate, respectively. Tillmann ( 2024 ) argues that supply chain pressure contributes to inflation. Inflation forecasting has also been a focus of research and is a popular topic in academia. Specifically, econometric and statistical forecasting models are preferred by researchers. For example, Marcellino et al. ( 2003 ), Ang et al. ( 2007 ), Clements and Galvão ( 2013 ) select the VAR model, and Forni et al. ( 2003 ), Eickmeier and Ziegler ( 2008 ), and Hall et al. ( 2023 ) adopt the dynamic factor analysis to predict inflation issues. Nakamura ( 2005 ), Szafranek ( 2019 ), and Almosova and Andresen ( 2023 ) use a neural network, and Wright ( 2009 ) and Hauzenberger et al. ( 2023 ) employ the Bayesian method and nonlinear dimension reduction techniques, respectively. Survey-based inflation forecasts have also been studied (Croushore, 2010 ; Faust and Wright, 2013 ; Huber et al. 2023 ). Moreover, due to the lead-lag relationship in the Phillips curve model, Chletsos et al. ( 2016 ), McKnight et al. ( 2020 ), and Bańbura and Bobeica ( 2023 ) forecast inflation based on the Phillips curve. Recently, advanced machine learning (ML) technology has been introduced to inflation forecasting. For example, Medeiros et al. ( 2021 ), Aras and Lisboa ( 2022 ), and Araujo and Gaglianone ( 2023 ) demonstrate the advantages of the ML model for predicting inflation in the United States, Turkey and Brazil, respectively; Rodríguez-Vargas ( 2020 ) uses two variants of k nearest neighbour, random forest, extreme gradient enhancement, and long short-term memory (LSTM) networks to evaluate inflation forecasts in Costa Rica.

Some novel factors have also been introduced in research on inflation. For example, Chen et al. ( 2014 ) introduce commodity price aggregates to inflation forecasts. Bec and De Gaye ( 2016 ) and Balcilar et al. ( 2017 ) incorporate metal price series and oil price forecast errors in predictive models for inflation and argue that such additions benefit inflation forecasting. Aparicio and Bertolotto ( 2020 ) combine online price indices and inflation and show that online price indices can forecast inflation well. Clements and Reade ( 2020 ), Rambaccussing and Kwiatkowski ( 2020 ), Mazumder ( 2021 ), and Simionescu ( 2022 ) investigate inflation from the perspective of behavioural finance, i.e., investor sentiment; however, the results seem contradictory. As the prices of agricultural products are important components of the whole price level, researchers have introduced agricultural products to inflation forecasts (Tule et al. 2019 ; Fasanya and Awodimila, 2020 ). Some factors that seem to be almost impossible to relate to inflation, such as climate variables and carbon market returns, have also been shown to significantly improve the accuracy of inflation forecasts (Boneva and Ferrucci, 2022 ; Xu et al. 2023 ). The list is far from exhaustive and exemplifies how active the field of connecting novel factors and inflation has been in recent years.

Behavioural finance has been shown to be an important factor in diverse financial and economic aspects and has become a research hotspot exhibiting numerous academic achievements in recent years (Adra and Barbopoulos, 2018 ; Audrino et al. 2020 ). Current investigations are based on two main aspects. The first focus relies on investor sentiments. For example, Fu et al. ( 2015 ) derive a sentiment-adjusted Markowitz efficient frontier; Kim and Ryu ( 2021 ) argue that investor sentiment is a determinant of investors’ trading decisions and behaviours; and Shen et al. ( 2023 ) investigate the effect of investor sentiment on new energy stock returns as well as value at risks (VaR) before and during COVID-19. Ryu et al. ( 2023 ) investigate the effects of sentiment on mispricing. Bashir et al. ( 2024 ) reveal a positive significant relation between stock price crash risk and investor sentiment. Another focus is investor attention. For example, Vozlyublennaia ( 2014 ) and Zhang et al. ( 2021 ) argue that investor attention can not only explain but also accurately forecast the stock market. Taking investor attention as a common financial variable, Han et al. ( 2018 ) and Wu et al. ( 2019 ) prove the importance of investor attention in the foreign exchange market, and Li et al. ( 2019 ), Chen et al. ( 2020 ), Zhang et al. ( 2022 ), Zhou et al. ( 2022 ), and Zhou et al. ( 2023 ) argue that investor attention matters in the crude oil market, internet financial market, commodity futures market and carbon market, respectively. Investor attention has also been proven to be an important pricing factor in the cryptocurrency market (Ibikunle et al. 2020 ; Zhu et al. 2021 ; Smales, 2022 ; Wan et al. 2023 ). Recently, investor attention and corporate ESG performance have been combined, and the results indicate that investor attention can significantly improve the ESG standards of listed companies (Zhang and Zhang, 2024 ). Contagion spillover based on investor attention has also been studied by scholars. For example, Li ( 2024 ) investigates the role of investor attention on the price of petroleum products (APPP) in forecasting Chinese stock market volatility.

As seen above, several novel factors are extending to research on inflation. However, relevant investigations are quite limited. Behavioural finance has developed rapidly and has shown to be important in finance and economics. On the one hand, investor sentiment, generated from behavioural finance, shows different empirical results regarding inflation. On the other hand, no investigation has attempted to link investor attention and inflation despite the fact that the two issues are naturally connected. This research gap sparks our interest in exploring the role of investor attention in inflation determination. Thus, in this paper, these connections are comprehensively researched. Specifically, this paper investigates inflation explaining and forecasting from the perspective of investor attention to enrich understanding in both behavioural finance and macroeconomics.

This paper selects the Google Search Volume Index (GSVI) from Google Trends to represent novel investor attention rather than other indicators, i.e., extreme return, abnormal trading volume, advertising expenditure, and media coverage, as GSVI shows advantages in terms of timeliness and information comprehensiveness (Zhang et al. 2022 ). As we aim to analyze the connection between investor attention and inflation in the United States, we set the search area to ‘America’ and directly searched for the keyword ‘inflation’ from the beginning of the GSVI in January 2004 to July 2020 to obtain data on monthly investor attention. For inflation, this paper chooses the consumer price index (CPI) inflation, as this indicator has a relatively high frequency and sufficient data for empirical investigation at a monthly frequency (Liu and Smith, 2014 ). Specifically, this study chooses the seasonally adjusted monthly CPI inflation for all urban consumers; as this indicator is officially more useful, we downloaded the related data freely from the Federal Reserve Economic Data. All the two series are transferred to logarithmic differences and are named with \({{Att}}_{t}\) and \({{Inf}}_{t}\) for further empirical research.

Some basic information on investor attention ( \({{Att}}_{t}\) ) and the CPI inflation rate ( \({{Inf}}_{t}\) ) is shown in Fig. 1 and Table 1 , respectively. As shown in Table 1 , the mean CPI inflation rate is positive, while the mean investor attention is negative, suggesting that investor attention and the inflation rate may be negatively connected. Furthermore, as we aim to analyse the selected data under the framework of the VAR model, an ADF-KPSS-PP joint stationary test is implemented to avoid the pseudo-regression phenomenon. The results show that the time series for the inflation rate or investor attention is stationary and can be directly used for VAR modelling. In addition, as shown in Fig. 1 , the extreme value of the CPI inflation rate always appears after the peak value in investor attention, which may further imply that investor attention affects the inflation rate. From both Fig. 1 and Table 1 , it is obvious that the time series for investor attention fluctuates more than that for the inflation rate.

Variation of investor attention and actual inflation from January 2004 to July 2020.

In this paper, as we analyze inflation explaining and forecasting, the full sample is divided into two parts. The first part, from January 2004 to August 2016, is used as an in-sample period for inflation explained through investor attention, and the remaining period, from September 2016 to July 2020, is used for out-of-sample forecasting.

Methodologies

Var and granger causality test.

The VAR model is a linear model that allows the variables to be depicted by several lagged indicators and is widely used in exploring the dynamic connections between financial variables (Guidolin and Hyde, 2012 ; Zhang and Lin, 2019 ). Thus, this paper adopts the widely used VAR model to explore the relationships between investor attention and the CPI inflation rate. Specifically, the VAR model used in this paper is specified by Eqs. ( 1 ) and ( 2 ) as follows:

where \({{Inf}}_{t}\) represents the inflation rate at time \(t\) , \({{Att}}_{t}\) refers to the current investor attention, n is the lag length for the VAR model, and \((t-i)\) represents the operator for the time lag. The linear Granger causality test examines the joint significance of all the lagged terms of one variable in a certain equation in the framework of the VAR model. Specific to the above VAR model represented by Eqs. ( 1 ) and ( 2 ), the interested Granger causality test is to determine whether the coefficients of ( \({\beta }_{11},\ldots ,{\beta }_{n1}\) ) are jointly equal to 0. Specifically, the significance of the Chi-square ( \({\chi }^{2}\) ) statistic is used to evaluate the results of the Granger causality test.

Linear regression models for inflation explanation and forecasting

As documented by previous studies, the oil market is a crucial external factor in inflation determination (Bec and De Gaye, 2016 ). Thus, in this paper, we control for non-negligible factors in the regression model to fully understand the role of investor attention in inflation. Specifically, according to Aparicio and Bertolotto ( 2020 ) and Zhou et al. ( 2023 ), the factor is introduced to the regression model based on the VAR model. The detailed regression model is shown in Eq. ( 3 ). Notably, the regression model incorporates the interaction terms between investor attention and the oil market as well:

where \({{oil}}_{t-i}\) represents the lagged oil market return and \({{Att}}_{t-i}\times {{oil}}_{t-i}\) represents the lagged interaction term between investor attention and the oil market return. As Eqs. ( 1 ) and ( 3 ) contain the ‘lead-lag’ relationship between the inflation rate and other variables, this paper extends these two equations and introduces the following out-of-sample forecasting models in Eqs. ( 4 ) and ( 5 ):

where \(\widehat{{{Inf}}_{t}}\) is the predicted inflation rate. To accurately forecast the inflation rate, in this study, the rolling window forecasting method is selected. In other words, in the case of out-of-sample forecasting, the window size remains fixed when the estimation window rolls forwards (Zhou et al. 2022 , Zhou et al. 2023 ).

Forecasting evaluation for out-of-sample data

In this paper, as we explore the role of investor attention in inflation forecasting, several evaluation indicators must be introduced. Following previous studies (Zhou et al. 2022 ; Zhou et al. 2023 ), this paper compares and assesses the accuracy of different predictive models by calculating the out-of-sample R squared ( \({R}_{{oos}}^{2}\) ), mean squared forecast error (MSFE), and MSFE-adjusted statistics. \({R}_{{oos}}^{2}\) is obtained by Eq. ( 6 ):

where T is the length of the full sample and \(t\) is the size of the rolling window. \({{Inf}}_{k}\) is the actual inflation rate. \(\hat{{{Inf}}_{k}}\) represents the forecasted value of the inflation rate from the predictive model. \(\widehat{{{Inf}}_{k}}\) denotes the predicted inflation rate of the benchmark forecasting model. A positive \({R}_{{oos}}^{2}\) indicates that the predictive model outperforms the benchmark model. Commonly, benchmark forecasts of inflation are obtained by three methods, i.e., forecasts from the autoregression model (AR), random walk (RW) model and Federal Reserve Greenbook (Adebiyi, 2007 ; Álvarez-Díaz and Gupta, 2016 ; Rossi and Sekhposyan, 2016 ). In this paper, we do not select the Federal Reserve’s Greenbook despite the fact that the forecast is made by the experts of monetary policy authority. The reason is as follows. The estimations of the Federal Reserve’s Greenbook are released to the public with a 5-year delay; if we choose the indicator as the benchmark, given the appearance of the GSVI in 2004, the selection may cause the data to be outdated and the sample size to be small. Thus, the remaining two models, i.e., AR and RW, are selected as the benchmarks. The MSFE can be obtained from Eq. ( 7 ):

Based on MSFE, Clark and McCracken ( 2001 ) developed the MSFE-adjusted statistic. The MSFE-adjusted statistic can further identify whether the out-of-sample forecasts are significant. Specifically, the MSFE-adjusted statistic is measured by Eq. ( 8 ):

where \({{MSFE}}_{a}\) and \({{MSFE}}_{b}\) denote the MSFE statistics of the predictive model and the benchmark model, respectively. \(\hat{{{Inf}}_{k,a}}\) and \(\hat{{{Inf}}_{k,b}}\) represent the forecast values of the inflation rate from the predictive model and the benchmark model, respectively.

Empirical results

Explanation of inflation.

The basic empirical process shows that the optimal lag length in the VAR model is 1. In other words, setting the lag length to 1 is sufficient to ensure the stability of the VAR model. Based on this lag length, this paper estimates the VAR model and implements the corresponding Granger causality test for the in-sample period. The related estimation results are shown in Table 2 . To further illustrate the stability of the VAR model, the AR root test is implemented, and the results are shown in Fig. 2 . The AR root test shows that all the characteristic roots are in the unit circle, indicating that the established VAR model has good stability.

A graphic description of the AR root test under the framework of VAR(1) model.

Two interesting discoveries are shown in Table 2 . First, current investor attention on inflation indeed has a negative impact on the inflation rate in the next observation period, as the value of \({{Att}}_{t-1}\) in the equation for the inflation rate in the VAR model is significantly negative. Second, the results from the Granger causality test show that investor attention to inflation does indeed Granger cause changes in the inflation rate. The VAR model allows researchers to understand the inflation rate’s reaction to the shock from investor attention under the framework of the impulse response function (IRF). Thus, this paper implements the related impulse response analysis and shows the results in Figs. 3 , 4 . As shown in Fig. 3 , once the inflation receives one unit shock from investor attention, the impact may last approximately 7 months.

Response of inflation rate to investor attention from January 2004 to August 2016 under VAR model.

Response of investor attention to the inflation rate from January 2004 to August 2016 under VAR model.

Equation ( 3 ) controls for the factor of the oil market and reinvestigates the relationship between investor attention and the inflation rate. According to Wang et al. ( 2023 ), the Brent oil futures market is an important market in the global oil market. Thus, this paper collects the returns of Brent oil futures to represent the oil market. The estimation results of Eq. ( 3 ) are shown in Table 3 . As shown, after controlling for the oil market factor in the last observation period, investor attention still has a significant negative impact on the current inflation rate.

In summary, investor attention has a negative impact on the inflation rate. This phenomenon may be attributed to the following reasons. First, when investor attention on inflation increases, a society may suffer from inflation in the current period, which may indicate that the public is concerned with currency devaluation in the future (Borensztein and De Gregorio, 1999 ). Therefore, investors may increase expenditures in the current period by converting paper money to commodities to purchase more products with the same amount of money. Thus, in the next period, total consumption demand in a society may decrease. According to the basic theory of demand and supply in macroeconomics, the price level will decrease in the next observation period. Thus, the inflation rate decreases. Second, according to the investor recognition hypothesis and limited attention theory (Merton, 1987 ), investors are aware of only a subset of the available assets in informationally incomplete markets; for neglected assets, a return premium is needed. Thus, investor attention should be negatively connected with asset returns. In addition, numerous investigations have empirically documented that an increase in investor attention is followed by a decrease in returns (Smales, 2021 ; Piñeiro-Chousa et al. 2020 ). This result implies that prices are decreasing; in other words, the price decreases, resulting in a negative correlation between investor attention and the inflation rate. The results on the significance of investor attention further demonstrate the importance in macroeconomics of considering not only traditional factors but also investor psychology and behaviour when investigating variations in the inflation rate.

All the above analyses show that investor attention has excellent explanatory power regarding the macro inflation rate. Furthermore, due to their significant explanatory power, the results can help authorities guide public attention to stabilize the inflation rate. However, this remarkable explanatory power is not enough to illustrate the crucial role of investor attention in inflation determination, as Welch and Goyal ( 2008 ) argue that out-of-sample tests seem to be more precise. Thus, the role of investor attention in inflation determination deserves further investigation. It is also worthwhile to explore whether investor attention can be used to forecast the inflation rate in the out-of-sample period. We show these results in the subsequent subsection.

Inflation forecasting

This paper first implements short-horizon forecasting to explore the predictive power of investor attention for the inflation rate, similar to the findings of numerous studies (Zhu et al. 2021 ; Zhang et al. 2022 ; Zhou et al. 2022 ; Zhou et al. 2023 ). Specifically, this paper forecasts the inflation rate for the next month based on current information about the inflation rate and investor attention. We set the lag length of the AR benchmark model to 1, as other lag lengths do not show an advantage in forecasting (Aparicio and Bertolotto, 2020 ). The out-of-sample forecasts are implemented based on Eqs. ( 4 ) and ( 5 ), and the accuracy evaluation is presented in Table 4 . As shown in Table 4 , the predictive models with investor attention significantly outperform the two benchmark models in all cases, as all the \({R}_{{oos}}^{2}\) values are greater than 0.05 and the MSFE-adjusted statistics are significant.

The above results indicate that investor attention is a nonnegligible factor in inflation determination, as investor attention can be used to explain and forecast inflation. However, as Zhou et al. ( 2023 ) note, models that work well for short-horizon forecasts may not perform well for long-horizon forecasts. Thus, it is also useful to explore whether investor attention can be used to forecast inflation over long horizons. Inspired by Zhu et al. ( 2021 ), Zhou et al. ( 2022 ) and Zhou et al. ( 2023 ), we implement relevant investigations. Specifically, we forecast the inflation rate two or three months ahead and compare the forecast performance with that of the AR (1) and RW benchmark models. The results are presented in Table 5 . As shown, in the long-horizon forecasts, the predictive models incorporating investor attention significantly outperform the AR (1) and RW benchmark models, as all the \({R}_{{oos}}^{2}\) values are larger than 0 and the MSFE-adjusted statistics are significant. The empirical results for long-horizon forecasts further confirm the importance of investor attention to inflation.

Robustness checks

The above results demonstrate the important role of investor attention in inflation. However, the results are obtained through investor attention on certain keywords of ‘inflation’ and variable or certain model specifications, which may lack the necessary rigor for an academic research paper. Thus, this paper implements two robustness checks. The first check is to update investor attention with other keywords searched by Google Trends. The second check is to alter the variable or model specifications. The related robustness checks are shown in the following subsections.

Update keywords

We search for another keyword, ‘monetary policy’, which is implemented by the central bank and is closely related to inflation. Based on the two keywords ‘inflation’ and ‘monetary policy’, we reperformed all the above empirical investigations to test our empirical results that investor attention can explain and forecast inflation. The estimation results are shown in Table 6 and Table 7 .

As shown in Table 6 , investor attention still has a negative impact on inflation and is still the Granger cause of inflation. In other words, the conclusion that investor attention can explain the inflation rate does not change.

Table 7 shows several interesting findings. First, if the RW model is selected as the benchmark model, both predictive models can outperform the benchmarks. Second, if the AR (1) model were selected as the benchmark, the forecasting results would vary. If the GSVI is represented by the sum of inflation and monetary policy, the predictive model can outperform the benchmark model; furthermore, the forecast accuracy is even greater than the results shown in Table 4 and Table 5 . However, if the GSVI is represented by monetary policy, the predictive models perform worse than the benchmark. In summary, Table 7 shows that updating the search term does not change the conclusion that the GSVI is a crucial factor in inflation determination.

Update model specifications

In this section, the variables and model specifications used to illustrate the impacts of investor attention on the inflation rate are adjusted to ensure adequate rigor for an academic research paper. Specifically, we make three changes. The first modification involves adding another control variable, i.e., the real interest rate, as the real interest rate is also an important factor in explaining inflation (Lanne, 2006 ). We collect real interest rate data from Federal Reserve Economic Data and update the model specification as follows in Eqs. ( 9 ) and ( 10 ). The second modification is to consider the higher moment of investor attention, as high-order moments are proven to be crucial factors for financial variables (Zhou et al. 2022 ). The model specification is shown in Eq. ( 11 ). The third modification involves changing the regression model to an interactive model according to Vozlyublennaia ( 2014 ); the model specification is shown in Eqs. ( 12 )-( 13 ).

The detailed regression results of the above equations are provided in Table 8 . As shown in Table 8 , the coefficient for investor attention is still significantly negative. Thus, we may conclude that changes in the model specification do not change our conclusion that investor attention has excellent explanatory power for inflation. In summary, the robustness checks in subsections 6.1 and 6.2 do not change our conclusion that investor attention has a nonnegligible influence on the inflation rate. Thus, H1 and H2 hold.

Further discussion: investor attention and inflation expectations

Investor attention reflected by searching is a key step in obtaining information to form inflation expectations in the current society, and inflation expectations are a crucial factor for determining actual inflation under the NKPC (Friedman, 1968 ; Chen and Lo, 2019 ; Larsen et al. 2021 ). Thus, it is natural to link investor attention and inflation expectations to better understand the impact of investor attention on inflation. However, relevant studies are lacking. This paper collects data on inflation expectations made by the University of Michigan, which is a survey-based inflation expectation and has been widely used in previous studies (Luoma and Luoto, 2009 ; Malmendier and Nagel, 2016 ). The measurement of inflation expectations has several advantages. For example, the survey covers a wide range of daily life for American households and can be used to evaluate the dynamics of inflation expectations and potential consumption in the future. In addition, the measurement is considered the most representative indicator of inflation and consumer behaviour. This paper visualizes the connection between investor attention and inflation expectations in Eq. ( 14 ). The model specification is based on the following considerations. According to Friedman ( 1968 ), during the formation of inflation expectations, information processing takes a certain amount of time; thus, time lags are introduced. Information processing capabilities vary for individuals; considering the sample of our dataset, twelve lags are introduced.

In Eq. ( 14 ), \({{Exp}}_{t}\) refers to the inflation expectation, \({Att}\) is the operator for investor attention, and i is the time lag. We estimate Eq. ( 14 ) and show the results in Table 9 . As shown below, the coefficients for investor attention are significant. However, these results are not sufficient to conclude that investor attention affects inflation expectations, as investor attention is regarded as a financial time series and potential multicollinearity exists. Thus, this paper further implements a variance inflation factor (VIF) test, and the results are shown in Table 10 . According to Table 10 , the regression model does not have serious multicollinearity problems because the values for the VIF tests are smaller than the commonly used criteria, i.e., 5. Thus, the results in Table 9 are significant, investor attention is a crucial external factor for inflation expectations, and H3 is supported.

Conclusions

The novelty of this paper is its confirmation of the important role of investor attention in inflation, which broadens the research fields of both behavioural finance and macroeconomics. The empirical process and results can be summarized as follows. First, we select several linear model specifications to conclude that investor attention and inflation are negatively connected. Second, we extend models of inflation explaining out-of-sample forecasts. The empirical results show that predictive models that incorporate investor attention can outperform the AR (1) and RW benchmark models in both short and long horizons. Third, this paper implements robustness checks, and investor attention can still explain and forecast inflation. Fourth, this paper proves that investor attention affects inflation through inflation expectations. In summary, investor attention matters in inflation determination.

The results shed light on several perspectives. For example, on the one hand, central banks may guide public opinions to control inflation to some extent; on the other hand, economic participants may adopt simple models to forecast inflation to avoid potential losses. However, deficiencies exist. First, adopting sophisticated models to further test the linear or nonlinear roles of investor attention is of interest. Second, this paper constructs a linear regression model for inflation expectation and investor attention. The information process is complex, and a linear model may not be applicable, considering alternative models and cumulative values of investor attention are also interesting issues. At a minimum, the two abovementioned deficiencies deserve further investigation.

Data availability

The search volume for “inflation” is retrieved from: https://trends.google.com/trends/explore?date=2004-01-01%202020-07-31&geo=US&q=inflation&hl=en-US . The search volume for “monetary policy” is retrieved from: https://trends.google.com/trends/explore?date=2004-01-01%202020-07-31&geo=US&q=monetary%20policy&hl=en-US . The data for consumer price index is retrieved from: https://fred.stlouisfed.org/series/CPIAUCSL . The data for Brent oil return is retrieved from: https://cn.investing.com/commodities/brent-oil-historical-data . The data for real interest rate is retrieved from: https://fred.stlouisfed.org/series/REAINTRATREARAT1MO . The data for inflation expectation is retrieved from: https://fred.stlouisfed.org/series/MICH .

Adebiyi MA (2007) Does money tell us anything about inflation in Nigeria? Singapore Econ Rev 52(01):117–134. https://doi.org/10.1142/S0217590807002592

Article   Google Scholar  

Adra S, Barbopoulos LG (2018) The valuation effects of investor attention in stock-financed acquisitions. J Empir Finance 45:108–125. https://doi.org/10.1016/j.jempfin.2017.10.001

Altissimo F, Mojon B, Zaffaroni P (2009) Can aggregation explain the persistence of inflation? J Monet Econ 56(2):231–241. https://doi.org/10.1016/j.jmoneco.2008.12.013

Álvarez-Díaz M, Gupta R (2016) Forecasting US consumer price index: does nonlinearity matter? Appl Econ 48(46):4462–4475. https://doi.org/10.1080/00036846.2016.1158922

Almosova A, Andresen N (2023) Nonlinear inflation forecasting with recurrent neural networks. J Forecast 42(2):240–259. https://doi.org/10.1002/for.2901

Article   MathSciNet   Google Scholar  

Ang A, Bekaert G, Wei M (2007) Do macro variables, asset markets, or surveys forecast inflation better? J Monet Econ 54(4):1163–1212. https://doi.org/10.1016/j.jmoneco.2006.04.006

Aparicio D, Bertolotto MI (2020) Forecasting inflation with online prices. Int J Forecast 36(2):232–247. https://doi.org/10.1016/j.ijforecast.2019.04.018

Aras S, Lisboa PJ (2022) Explainable inflation forecasts by machine learning models. Expert Syst Appl 207:117982. https://doi.org/10.1016/j.eswa.2022.117982

Araujo GS, Gaglianone WP (2023) Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models. Latin Am J Cent Bank 4(2):100087. https://doi.org/10.1016/j.latcb.2023.100087

Audrino F, Sigrist F, Ballinari D (2020) The impact of sentiment and attention measures on stock market volatility. Int J Forecast 36(2):334–357. https://doi.org/10.1016/j.ijforecast.2019.05.010

Balcilar M, Katzke N, Gupta R (2017) Do precious metal prices help in forecasting South African inflation? North Am J Econ Finance 40:63–72. https://doi.org/10.1016/j.najef.2017.01.007

Bańbura M, Bobeica E (2023) Does the Phillips curve help to forecast euro area inflation? Int J Forecast 39(1):364–390. https://doi.org/10.1016/j.ijforecast.2021.12.001

Bashir U, Kayani UN, Khan S, Polat A, Hussain M, Aysan AF (2024) Investor sentiment and stock price crash risk: The mediating role of analyst herding. Comput Hum Behav Rep, 100371. https://doi.org/10.1016/j.chbr.2024.100371

Bec F, De Gaye A (2016) How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US, French and UK inflation forecasts. Economic Modelling 53:75–88. https://doi.org/10.1016/j.econmod.2015.11.008

Boneva L, Ferrucci G (2022) Inflation and climate change: the role of climate variables in inflation forecasting and macro modelling. This paper can be achieved at: https://eprints.lse.ac.uk/115533/1/INSPIRE_Sustainable_Central_Banking_Toolbox_Policy_Briefing_Paper_1.pdf

Borensztein E, De Gregorio J (1999) Devaluation and inflation after currency crises. International Monetary Fund, 1-34. This paper can be achieved at: https://www.researchgate.net/publication/245906019_Devaluation_and_Inflation_after_Currency_Crises

Bouras P, Bustamante C, Guo X, Short J (2023) The contribution of firm profits to the recent rise in inflation. Econ Lett 233:111449. https://doi.org/10.1016/j.econlet.2023.111449

Cai H, Jiang Y, Liu X (2022) Investor attention, aggregate limit-hits, and stock returns. Int Rev Financ Anal 83:102265. https://doi.org/10.1016/j.irfa.2022.102265

Canova F, Ferroni F (2012) The dynamics of US inflation: Can monetary policy explain the changes? J Econom 167(1):47–60. https://doi.org/10.1016/j.jeconom.2011.08.008

Chen HY, Lo TC (2019) Online search activities and investor attention on financial markets. Asia Pac Manage Rev 24(1):21–26. https://doi.org/10.1016/j.apmrv.2018.11.001

Chen R, Qian Q, Jin C, Xu M, Song Q (2020) Investor attention on Internet financial markets. Finance Res Lett 36:101421. https://doi.org/10.1016/j.frl.2019.101421

Chen YC, Turnovsky SJ, Zivot E (2014) Forecasting inflation using commodity price aggregates. J Econom 183(1):117–134. https://doi.org/10.1016/j.jeconom.2014.06.013

Chen Q, Zhu H, Yu D, Hau L (2022) How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis. N Am J Econ Finance 59:101581. https://doi.org/10.1016/j.najef.2021.101581

Chletsos M, Drosou V, Roupakias S (2016) Can Phillips curve explain the recent behavior of inflation? Further evidence from USA and Canada. J Econ Asymmetr 14:20–28. https://doi.org/10.1016/j.jeca.2016.07.005

Choi Y (2021) Inflation dynamics, the role of inflation at different horizons and inflation uncertainty. Int Rev Econ Finance 71:649–662. https://doi.org/10.1016/j.iref.2020.10.004

Ciccarelli M, Mojon B (2010) Global inflation. Rev Econ Stat 92(3):524–535. https://doi.org/10.1162/REST_a_00008

Clark TE, McCracken MW (2001) Tests of equal forecast accuracy and encompassing for nested models. J Econom 105(1):85–110. https://doi.org/10.1016/S0304-4076(01)00071-9

Clements MP, Galvão AB (2013) Forecasting with vector autoregressive models of data vintages: US output growth and inflation. Int J Forecast 29(4):698–714. https://doi.org/10.1016/j.ijforecast.2011.09.003

Clements MP, Reade JJ (2020) Forecasting and forecast narratives: The Bank of England inflation reports. Int J Forecast 36(4):1488–1500. https://doi.org/10.1016/j.ijforecast.2019.08.013

Coibion O, Gorodnichenko Y (2015) Is the Phillips curve alive and well after all? Inflation expectations and the missing disinflation. Am Econ J: Macroecon 7(1):197–232. https://doi.org/10.1257/mac.20130306

Croushore D (2010) An evaluation of inflation forecasts from surveys using real-time data. The BE J Macroecon, 10(1). https://doi.org/10.2139/ssrn.940418

Effah Nyamekye G, Adusei Poku E (2017) What is the effect of inflation on consumer spending behaviour in Ghana? This paper can be achieved at: https://mpra.ub.uni-muenchen.de/81081/1/MPRA_paper_81081.pdf

Eickmeier SZiegler C (2008) How successful are dynamic factor models at forecasting output and inflation? A meta‐analytic approach. J Forecast 27(3):237–265. https://doi.org/10.1002/for.1056

Fama EF (1975) Short-term interest rates as predictors of inflation. The American Economic Review, 65(3), 269-282. This paper can be achieved at: http://www.ressources-actuarielles.net/EXT/ISFA/1226.nsf/8d48b7680058e977c1256d65003ecbb5/38019a3264b83e49c125751e0055f911/$FILE/Mod%C3%A8le%20de%20Fama%20pour%20l’inflation.pdf

Faust J, Wright JH (2013) Forecasting inflation. Handbook of Economic Forecasting 2013 2:2–56. 10.1016/B978-0-444-53683-9.00001-3

Fasanya IO, Awodimila CP (2020) Are commodity prices good predictors of inflation? The African perspective. Resources Policy 69:101802. https://doi.org/10.1016/j.resourpol.2020.101802

Forni M, Hallin M, Lippi M, Reichlin L (2003) Do financial variables help forecasting inflation and real activity in the Euro area? J Monet Econ 50(6):1243–1255. https://doi.org/10.1016/S0304-3932(03)00079-5

Forbes K, Kirkham L, Theodoridis K (2021) A trendy approach to UK inflation dynamics. Manchester School 89:23–75. https://doi.org/10.1111/manc.12293

Friedrich C (2016) Global inflation dynamics in the post-crisis period: What explains the puzzles? Econ Lett 142:31–34. https://doi.org/10.1016/j.econlet.2016.02.032

Friedman M (1968) The role of monetary policy. Am Econ Rev 58:1–17. https://doi.org/10.1007/978-1-349-24002-9_11

Fu C, Jacoby G, Wang Y (2015) Investor sentiment and portfolio selection. Finance Res Lett 15:266–273. https://doi.org/10.1016/j.frl.2015.11.004

Guidolin M, Hyde S (2012) Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective. J Bank Finance 36(3):695–716. https://doi.org/10.1016/j.jbankfin.2011.10.011

Hakkio CS (2009) Global inflation dynamics. Federal Reserve Bank of Kansas City Working Paper. https://doi.org/10.2139/ssrn.1335348

Hall SG, Tavlas GS, Wang Y (2023) Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations. J Forecast 42(3):514–529. https://repec.cal.bham.ac.uk/pdf/22-12.pdf

Han L, Wu Y, Yin L (2018) Investor attention and currency performance: International evidence. Appl Econ 50(23):2525–2551. https://doi.org/10.1080/00036846.2017.1403556

Hauzenberger N, Huber F, Klieber K (2023) Real-time inflation forecasting using non-linear dimension reduction techniques. Int J Forecast 39(2):901–921. https://doi.org/10.1016/j.ijforecast.2022.03.002

Huber F, Onorante L, Pfarrhofer M (2023) Forecasting euro area inflation using a huge panel of survey expectations. Int J Forecast. https://doi.org/10.1016/j.ijforecast.2023.09.003

Ibikunle G, McGroarty F, Rzayev K (2020) More heat than light: Investor attention and bitcoin price discovery. Int Rev Financ Anal 69:101459. https://doi.org/10.1016/j.irfa.2020.101459

Kim K, Ryu D (2021) Does sentiment determine investor trading behaviour? Appl Econ Lett 28(10):811–816. https://doi.org/10.1080/13504851.2020.1782331

Larsen VH, Thorsrud LA, Zhulanova J (2021) News-driven inflation expectations and information rigidities. J Monet Econ 117:507–520. https://doi.org/10.1016/j.jmoneco.2020.03.004

Lanne M (2006) Nonlinear dynamics of interest rate and inflation. J Appl Econom 21(8):1157–1168. https://doi.org/10.1002/jae.908

Li S, Zhang H, Yuan D (2019) Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests. Energy Econ 84:104494. https://doi.org/10.1016/j.eneco.2019.104494

Li D (2024) Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products. Int Rev Econ Finance 90:115–122. https://doi.org/10.1016/j.iref.2023.11.015

Liu TY, Ma JT (2023) Exchange rate and inflation between China and the United States: A bootstrap rolling-window approach. Econ Syst, 101152. https://doi.org/10.1016/j.ecosys.2023.101152

Liu Y, Niu Z, Suleman MT, Yin L, Zhang H (2022) Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework. Energy 238:121779. https://doi.org/10.1016/j.energy.2021.121779

Liu D, Smith JK (2014) Inflation forecasts and core inflation measures: Where is the information on future inflation? Q Rev Econ Finance 54(1):133–137. https://doi.org/10.1016/j.qref.2013.07.006

Luoma A, Luoto J (2009) Modelling the general public’s inflation expectations using the Michigan survey data. Appl Econ 41(10):1311–1320. https://doi.org/10.1080/00036840701604339

Malmendier U, Nagel S (2016) Learning from inflation experiences. Q J Econ 131(1):53–87. https://doi.org/10.1093/qje/qjv037

Marcellino M, Stock JH, Watson MW (2003) Macroeconomic forecasting in the euro area: Country specific versus area-wide information. Eur Econ Rev 47(1):1–18. https://doi.org/10.1016/S0014-2921(02)00206-4

Mavroeidis S, Plagborg-Møller M, Stock JH (2014) Empirical evidence on inflation expectations in the New Keynesian Phillips Curve. Am Econ J: J Econ Lit 52(1):124–188. https://doi.org/10.1257/jel.52.1.124

Mazumder S (2021) The reaction of inflation forecasts to news about the Fed. Econ Model 94:256–264. https://doi.org/10.1016/j.econmod.2020.09.026

McKnight S, Mihailov A, Rumler F (2020) Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend. Econ Model 87:383–393. https://doi.org/10.1016/j.econmod.2019.08.011

Medeiros MC, Vasconcelos GF, Veiga Á, Zilberman E (2021) Forecasting inflation in a data-rich environment: the benefits of machine learning methods. J Bus Econ Stat 39(1):98–119. https://doi.org/10.1080/07350015.2019.1637745

Merton RC (1987) A simple model of capital market equilibrium with incomplete information. Available at: https://dspace.mit.edu/bitstream/handle/1721.1/2166/SWP-1869-18148074.pdf

Monacelli T, Sala L (2009) The international dimension of inflation: Evidence from disaggregated consumer price data. J Money Credit Bank 41:101–120. https://doi.org/10.1111/j.1538-4616.2008.00200.x

Mumtaz H, Surico P (2012) Evolving international inflation dynamics: world and country-specific factors. J Eur Econ Assoc 10(4):716–734. https://doi.org/10.1111/j.1542-4774.2012.01068.x

Murphy RG (2014) Explaining inflation in the aftermath of the Great Recession. J Macroecon 40:228–244. https://doi.org/10.1016/j.jmacro.2014.01.002

Nakamura E (2005) Inflation forecasting using a neural network. Econ Lett 86(3):373–378. https://doi.org/10.1016/j.econlet.2004.09.003

Piñeiro-Chousa J, López-Cabarcos MÁ, Ribeiro-Soriano D (2020) Does investor attention influence water companies’ stock returns? Technol Forecast Soc Change 158:120115. https://doi.org/10.1016/j.techfore.2020.120115

Rambaccussing D, Kwiatkowski A (2020) Forecasting with news sentiment: Evidence with UK newspapers. Int J Forecast 36(4):1501–1516. https://doi.org/10.1016/j.ijforecast.2020.04.002

Rossi B, Sekhposyan T (2016) Forecast rationality tests in the presence of instabilities, with applications to Federal Reserve and survey forecasts. J Appl Econom 31(3):507–532. https://doi.org/10.1002/jae.2440

Rodríguez-Vargas A (2020) Forecasting Costa Rican inflation with machine learning methods. Latin Am J Cent Bank 1(1-4):100012. https://doi.org/10.1016/j.latcb.2020.100012

Ryu D, Ryu D, Yang, H (2023) Investor sentiment and futures market mispricing. Finance Res Lett, 58, https://doi.org/10.1016/j.frl.2023.104559

Simionescu M (2022) Econometrics of sentiments-sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis. Technol Forecast Soc Change 182:121867. https://doi.org/10.1016/j.techfore.2022.121867

Smales LA (2022) Investor attention in cryptocurrency markets. Int Rev Financ Anal 79:101972. https://doi.org/10.1016/j.irfa.2021.101972

Shen Y, Liu C, Sun X, Guo K (2023) Investor sentiment and the Chinese new energy stock market: A risk–return perspective. Int Rev Econ Finance 84:395–408. https://doi.org/10.1016/j.iref.2022.11.035

Smales LA (2021) Investor attention and global market returns during the COVID-19 crisis. Int Rev Financ Anal 73:101616. https://doi.org/10.1016/j.irfa.2020.101616

Szafranek K (2019) Bagged neural networks for forecasting Polish (low) inflation. Int J Forecast 35(3):1042–1059. https://doi.org/10.1016/j.ijforecast.2019.04.007

Tillmann P (2024) The asymmetric effect of supply chain pressure on inflation. Econ Lett, 111540. https://doi.org/10.1016/j.econlet.2024.111540

Tule MK, Salisu AA, Chiemeke CC (2019) Can agricultural commodity prices predict Nigeria’s inflation? J Commod Mark 16:100087. https://doi.org/10.1016/j.jcomm.2019.02.002

Vozlyublennaia N (2014) Investor attention, index performance, and return predictability. J Bank Finance 41:17–35. https://doi.org/10.1016/j.jbankfin.2013.12.010

Wan J, Wu Y, Zhu P (2023) The COVID-19 pandemic and Bitcoin: Perspective from investor attention. Front Public Health 11:1147838. https://doi.org/10.3389/fpubh.2023.1147838

Article   PubMed   PubMed Central   Google Scholar  

Wang S, Feng H, Gao D (2023) Testing for short explosive bubbles: A case of Brent oil futures price. Finance Res Lett 52:103497. https://doi.org/10.1016/j.frl.2022.103497

Welch I, Goyal A (2008) A comprehensive look at the empirical performance of equity premium prediction. Rev Financ Stud 21(4):1455–1508. https://doi.org/10.1093/rfs/hhm014

Wong B (2015) Do inflation expectations propagate the inflationary impact of real oil price shocks?: Evidence from the Michigan survey. J Money Credit Bank 47(8):1673–1689. https://doi.org/10.1111/jmcb.12288

Woodford M, Walsh CE (2005) Interest and prices: Foundations of a theory of monetary policy. Macroecon Dyn 9(3):462–468. https://doi.org/10.1017/S1365100505040253

Wright JH (2009) Forecasting US inflation by Bayesian model averaging. J Forecast 28(2):131–144. https://doi.org/10.1002/for.1088

Wu Y, Han L, Yin L (2019) Our currency, your attention: Contagion spillovers of investor attention on currency returns. Econ Model 80:49–61. https://doi.org/10.1016/j.econmod.2018.05.012

Xu Y, Li X, Yuan P, Zhang Y (2023) Trade-off between environment and economy: The relationship between carbon and inflation. Front Environ Sci 11:334. https://doi.org/10.3389/fenvs.2023.1093528

Yellen JL (2017) Inflation, uncertainty, and monetary policy. Bus Econ 52(4):194–207. https://doi.org/10.1057/s11369-017-0057-x

Zakaria M, Khiam S, Mahmood H (2021) Influence of oil prices on inflation in South Asia: Some new evidence. Resour Policy 71:102014. https://doi.org/10.1016/j.resourpol.2021.102014

Zhang Y, Chen Y, Wu Y, Zhu P (2022) Investor attention and carbon return: Evidence from the EU-ETS. Econ Res-Ekonomska Istraživanja 35(1):709–727. https://doi.org/10.1080/1331677X.2021.1931914

Zhang Y, Chu G, Shen D (2021) The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Res Lett 38:101484. https://doi.org/10.1016/j.frl.2020.101484

Zhang YJ, Lin JJ (2019) Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors? Int Rev Financ Anal 66:101395. https://doi.org/10.1016/j.irfa.2019.101395

Zhang Z, Zhang L (2024) Investor attention and corporate ESG performance. Finance Res Lett 60:104887. https://doi.org/10.1016/j.frl.2023.104887

Zhou Q, Zhu P, Wu Y, Zhang Y (2022) Research on the volatility of the cotton market under different term structures: perspective from investor attention. Sustainability 14(21):14389. https://doi.org/10.3390/su142114389

Zhou Q, Zhu P, Zhang Y (2023) Contagion spillover from bitcoin to carbon futures pricing: perspective from investor attention. Energies 16:929. https://doi.org/10.3390/en16020929

Zhu P, Zhang X, Wu Y, Zheng H, Zhang Y (2021) Investor attention and cryptocurrency: Evidence from the Bitcoin market. PLoS One 16(2):e0246331. https://doi.org/10.1371/journal.pone.0246331

Article   CAS   PubMed   PubMed Central   Google Scholar  

Download references

This work was funded by the Key Program of the National Social Science Foundation of China (21ATJ007).

Author information

Authors and affiliations.

School of Economics, Beijing Technology and Business University, Beijing, 100048, China

Panpan Zhu & Qingjie Zhou

School of Economics and Management, Shandong Agricultural University, Taian, 271018, China

Yinpeng Zhang

You can also search for this author in PubMed   Google Scholar

Contributions

Panpan Zhu contributed to the “Literature review”, “Data”, “Empirical results” and “Robustness checks” sections. Qingjie Zhou contributed to “Methodologies” and “Further discussion”. Yinpeng Zhang contributed to the conceptional design, “Introduction”, and “Conclusions”. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Yinpeng Zhang .

Ethics declarations

Competing interests.

The authors declare no competing interest.

Ethical approval

Ethical approval was not required as the study did not involve human participants.

Informed consent

Informed consent was not required as the study did not involve human participants.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Zhu, P., Zhou, Q. & Zhang, Y. Investor attention and consumer price index inflation rate: Evidence from the United States. Humanit Soc Sci Commun 11 , 541 (2024). https://doi.org/10.1057/s41599-024-03036-y

Download citation

Received : 22 April 2023

Accepted : 16 April 2024

Published : 29 April 2024

DOI : https://doi.org/10.1057/s41599-024-03036-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Working Papers

People’s Understanding of Inflation

• Author: Alberto Binetti , Francesco Nuzzi , Stefanie Stantcheva
• Authors: Alberto Binetti , Francesco Nuzzi , Stefanie Stantcheva

Publication: Harvard University Working Paper, 2024

This paper studies people’s understanding of inflation–their perceived causes, consequences, trade-offs–and the policies supported to mitigate its effects. We design a new, detailed online survey based on the rich existing literature in economics with two experimental components—a conjoint experiment and an information experiment—to examine how well public views align with established economic theories. Our key findings show that the major perceived causes of inflation include government actions, such as increased foreign aid and warrelated expenditures, alongside rises in production costs attributed to recent events like the COVID-19 pandemic, oil price fluctuations, and supply chain disruptions. Respondents anticipate many negative consequences of inflation but the most noted one is the increased complexity and difficulty in household decision-making. Partisan differences emerge distinctly, with Republicans more likely to attribute inflation to government policies and foresee broader negative outcomes, whereas Democrats anticipate greater inequality effects. Inflation is perceived as an unambiguously negative phenomenon without any potential positive economic correlates. Notably, there is a widespread belief that managing inflation can be achieved without significant trade-offs, such as reducing economic activity or increasing unemployment. These perceptions are hard to move experimentally. In terms of policy responses, there is resistance to monetary tightening, consistent with the perceived absence of trade-offs and the belief that it is unnecessary to reduce economic activity to fight inflation. The widespread misconception that inflation rises following increases in interest rates even leads to support for rate cuts to reduce inflation. There is a clear preference for policies that are perceived to have other benefits, such as reducing government debt in progressive ways or increasing corporate taxes, and for support for vulnerable households, despite potential inflationary effects.

• Online Appendix
• Questionnaire
• Summary Twitter Thread

Organization

• Join Our Team
• Updates, Events & News
• Publications
• Resources & Data
• [email protected]
• © 2024 Social Economics Lab. All rights reserved.
• Privacy Policy

• Main navigation
• Main content

Center for Inflation Research

The Cleveland Fed is home to the Center for Inflation Research, a leading resource for “all things inflation.” Explore commentary, research, and analysis on inflationary trends from the Center, and find out about upcoming inflation-related events.

Our inflation resources

Inflation indicators and data, our center's latest news, our inflation research.

Economic Commentaries About Inflation

Economic Commentaries provide analysis of relevant economic issues and are written for an informed but nonspecialist audience. Browse the Economic Commentaries about inflation written by our economists.

Working Papers About Inflation

Working Papers are preliminary versions of technical papers containing results and discussions of current research. Written for eventual publication in professional economics journals. Browse the Working Papers about inflation written by our economists.

Our Inflation Resources

About the Center for Inflation Research

The Center for Inflation Research is guided by economists at the Federal Reserve Bank of Cleveland and an advisory council consisting of leading experts from the United States and abroad who provide external perspectives and input into the Center's projects and conferences.

Inflation 101

Here is your guide to understanding inflation. The Cleveland Fed's Center for Inflation Research provides inflation basics and explanations of inflation essentials for you to explore.

Conferences: Center for Inflation Research

Find information about our dedicated inflation conferences, papers presented at our past conferences, and our sponsorship of conference sessions.

Measures of Expected Inflation

We explain why expected inflation is important to consumers and the Federal Reserve and how it affects actual inflation. We explain the two basic approaches to measuring them, model-based and survey-based measures.

Consumer Price Data

We explain how measures of consumer prices are computed and what the differences are between the consumer price index (CPI) and the personal consumption expenditures (PCE) price index. We explain various measures used to gauge underlying inflation, or the long-term trend in prices, such as medians and other trimmed-mean measures and core measures of inflation.

Inflation Indicators and Data

The median CPI is a measure of inflation computed by the Federal Reserve Bank of Cleveland. It ranks the components of CPI inflation and picks the one in the middle. Its construction makes it less sensitive to short-lived price fluctuations, thereby better capturing the trend in prices. Released monthly.

Median PCE Inflation

Median PCE inflation is a measure of inflation computed by the Federal Reserve Bank of Cleveland. It ranks the components of PCE inflation and picks the one in the middle. Its construction makes it less sensitive to short-lived price fluctuations, thereby better capturing the trend in prices. Released monthly.

Inflation Nowcasting

The Federal Reserve Bank of Cleveland provides daily “nowcasts” of inflation for two popular price indexes, the price index for personal consumption expenditures (PCE) and the Consumer Price Index (CPI). These nowcasts give a sense of where inflation is today. Released each business day.

Inflation Expectations

We report average expected inflation rates over the next one through 30 years. Our estimates of expected inflation rates are calculated using a Federal Reserve Bank of Cleveland model that combines financial data and survey-based measures. Released monthly.

Survey of Firms' Inflation Expectations

The Survey of Firms’ Inflation Expectations (SoFIE) is a large quarterly representative panel of firms in the manufacturing and services sectors that was created to measure inflation expectations of chief executive officers (CEOs) in the United States. Released quarterly.

Inflation Charting

Examine and compare the behavior of various measures of total inflation and underlying inflation.

Center updates, direct to your inbox

Register to receive the Center for Inflation Research  newsletter and other notifications by email. (Rest assured, we won’t share your information, and you can unsubscribe at any time.)

Read the most recent issue.

Learn more about our center

Lowering inflation: the who, what, and how.

CfIR leaders and experts from other Banks discuss how the Federal Reserve works to control inflation

https://youtu.be/qVgW2ozAKz8

Watch our leaders discuss what motivated the launch of our Center and the data and indicators available on this website.

Global Factors Driving Inflation and Monetary Policy: A Global VAR Assessment

• Published: 21 August 2020
• Volume 26 , pages 225–247, ( 2020 )

Cite this article

• Martin Feldkircher 1 , 2 &
• Gabriele Tondl   ORCID: orcid.org/0000-0001-7898-4474 3  

658 Accesses

3 Citations

2 Altmetric

Explore all metrics

This paper examines the nexus between inflation and central bank interest rate policies in inflation-targeting countries. First, it looks at the role of inflation among other factors affecting monetary policy. Second, it looks at the drivers of inflation alone. Thereby, the role of international spillovers from other countries is explicitly regarded as well, with the aim of assessing the extent to which inflation is driven by other countries’ inflation or monetary policy is influenced by other economies. The empirical study covers advanced countries and emerging market economies for the period 1995 - 2016 and uses Bayesian global vector autoregression to model external linkages and to account for variable uncertainty. This study finds that inflation plays an important role for monetary policy. More importantly, central banks clearly consider additional factors and notably other countries’ key indicators when setting interest rates. Inflation in turn is determined by both internal and external factors like the exchange rate and other countries’ inflation. These findings are much more comprehensive than previous literature and demonstrate that central banks consider a multitude of domestic and global factors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Some Empirical Evidence on the Effects of Monetary Policy in India: A Vector Autoregressive Based Analysis

The role of the exchange rate in canadian monetary policy: evidence from a tvp-bvar model.

The effect of central bank credibility on economic growth and output volatility in inflation targeting regime

Taylor ( 2007 ) proposed that CBs should explicitly watch the events in other economies, observe monetary decisions of other CBs and ideally act in a cooperative manner.

Only Galesi and Lombardi, ( 2009 ) studied inflation in a similar setting using a GVAR model, but with an outdated data set and smaller country coverage. Recently, Feldkircher and Siklos, ( 2019 ) used a similar econometric approach but focused on the link between inflation and inflation expectations for a large set of countries.

The sample covers all major economies and thus encompasses all major trade and financial linkages worldwide. These countries have an intra-group trade share of 60-90 percent.

For a comparison of models that deal with large data sets, such as panel VARs, GVARs and factor augmented VARs, see Feldkircher et al. ( 2020b ).

For simplicity it was assumed here that all countries feature the same number of endogenous variables in x j t . In the empirical application, the dimension of \({x}_{it}^{*}\) depends on the country.

Recently, other weights based on financial flows have been proposed in the literature (Eickmeier and Ng 2015 ). However, a sensitivity analysis of Feldkircher and Huber ( 2016 ) of weighting measures in Bayesian GVAR specifications showed that trade weights lead to a perfect model fit.

For further information on the specific posterior moments and hyperparameter specifications, see Feldkircher and Huber ( 2016 ). Due to storage limits a thinning interval was used to select 6,000 out of the 30,000 posterior draws. From these, unstable posterior draws were identified, which are characterized by large eigenvalues of the companion form of the global model leading to approximately 27% of the 6,000 posterior draws upon which the empirical results are based.

Traditionally, the inflation literature (Boschi and Girardi 2007 ; Alexova 2012 ) distinguishes demand-pull and cost-push (wage costs, import prices) factors for inflation. Due to data constraints, wage costs were not included in our model.

A related strand of literature investigated the effect of globalization, given by trade and financial openness, on the inflation level (e.g., Badinger 2009 ; Gnan and Valderrama, 2006 ).

Note that in the strict Taylor rule, CBs would set their interest rates according to deviation of inflation from a target and the output gap.

In this sample, all advanced countries officially or unofficially introduced IT in the 1990s and EMEs started doing so since 2000. Only China has yet not done so (Combes et al., 2017 ; Jahan 2017 ; Benes et al., 2017 ).

Note that the Fisher effect would also postulate a co-movement between inflation and interest rates.

This is in contrast to the finding of Choi et al. ( 2018 ), who reported that the effect of the oil price on inflation is no longer found after 2 years.

Note that the Brent oil price index is higher than the Russian, Mexican and Organisation of the Petroleum Exporting Countries (OPEC) oil price.

For India, an equally high impact of demand-driven inflation as in our results was found by Mohanty and John ( 2015 ), but only for the non-crisis period.

Also Huang et al. ( 2010 ) indicated liquidity as a major inflationary force in China.

We are aware that short-term interest rates may deviate from the interest rates set by CBs if the interest pass-through is distorted.

Only the Bank of England has published guidelines which explicitly state that interest rate policy should be guided by multiple domestic and international indicators, such as output growth, the exchange rate and developments in the U.S., the EA and EMEs.

Alexova, M. (2012). What determines inflation? International Journal of Monetary Economics and Finance , 5 (4), 345–369.

Google Scholar  

Auer, R., Borio, C., & Filardo, A. (2017). The globalisation of inflation: the growing importance of global value chains. BIS Working Papers, 602. https://www.bis.org/publ/work602.pdf Accessed 12 March 2020.

Badinger, H. (2009). Globalization, the output-inflation tradeoff and inflation. European Economic Review , 53 (8), 888–907.

Baǹbura, M., Giannone, D., & Reichlin, L. (2010). Large bayesian vector auto regressions. Journal of Applied Econometrics , 25 (1), 71–92.

Bank for International Settlements. (2014). 84th Annual Report. Bank for International Settlement, Basel, https://www.bis.org/publ/arpdf/ar2014e.pdf . Accessed 12 March 2020.

Benes, J., Clinton, K., Asish, G., Joice, J., Kamenik, O., Laxton, D., Mitra, P., Nadhanael, G., Wang, H., & Zhang, F. (2017). Inflation-forecast targeting for India : An outline of the analytical framework. IMF Working Paper, 17/32. https://www.imf.org/en/Publications/WP . Accessed 12 March 2020.

Bernanke, B., Boivin, J., & Eliasz, P. (2005). Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics , 120 (1), 387–422.

Boschi, M., & Girardi, A. (2007). Euro area inflation: long-run determinants and short-run dynamics. Applied Financial Economics , 17 (1), 9–24.

Calvo, G., & Reinhart, C. (2002). Fear of floating. Quarterly Journal of Economics , 117 (5), 379–408.

Canova, F., & Ciccarelli, M. (2013). Panel vector autoregressive models: a survey. VAR Models in macroeconomics–new developments and applications: Essays in honor of Christopher A. Sims. Emerald Group Publishing Limited (pp. 205–246).

Caporale, G., Helmi, M., Catik, A., Faek, M., & Akdeniz, C. (2018). Monetary policy rules in emerging countries: Is there an augmented nonlinear Taylor rule? Economic Modelling , 72 (June), 306–319.

Chatterjee, A. (2016). Globalization and monetary policy comovement: International evidence. Journal of International Money and Finance , 68 , 181–202.

Chin, M., Graeve, F.D., Filippeli, T., & Theodoridis, K. (2018). Understanding International Long-Term Interest Rate Comovement. Cardiff Economics Working Papers E2018/19, Cardiff University, Cardiff Business School, Economics Section. https://ideas.repec.org/p/cdf/wpaper/2018-19.html . Accessed 12 March 2020.

Choi, S., Furceri, D., Loungani, P., Mishra, S., & Poplawski-Ribeiro, M. (2018). Oil prices and inflation dynamics:, Evidence from advanced and developing economies. Journal of International Money and Finance , 82 (C), 71–96.

Ciccarelli, M., & Mojon, B. (2005). Is inflation a global phenomenon? ECB Research Bulletin , 3 , 6–7. https://ideas.repec.org/a/ecb/ecbrbu/200500032.html . Accessed 12 March 2020.

Coe, D.T., & McDermott, C.J. (1997). Does the gap model work in Asia? IMF Staff Papers , 44 (1), 59–80. https://www.elibrary.imf.org/view/IMF024/15875-9781451957112/15875-9781451957112/15875-9781451957112_A003.xml?language=en . Accessed 12 March 2020.

Cogley, T., & Sargent, T.J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics , 8 (2), 262–302.

Combes, J., Debrun, X., & Tapsoba, A. (2017). Inflation Targeting, fiscal rules and the policy mix: Cross-effects and interactions. The Economic Journal , 128 , 2755–2784.

de Oliveira, F.N., & Petrassi, M. (2010). Is Inflation Persistence Over? Working Papers Series 230, Central Bank of Brazil, Research Department. https://ideas.repec.org/p/bcb/wpaper/230.html . Accessed 12 March 2020.

Dees, S., Mauro, F. d., Pesaran, M.H., & Smith, L.V. (2007). Exploring the international linkages of the euro area: a global VAR analysis. Journal of Applied Econometrics , 22 (1), 1–38.

Defever, F., Fischer, C., & Suedekum, J. (2016). Relational contracts and supplier turnover in the global economy. Journal of International Economics , 103 (C), 147–165.

Del Negro, M., Giannone, D., Giannoni, M., & Tambalotti, A. (2018). Global trends in interest rates. https://ideas.repec.org/p/fip/feddwp/1812.html . Working paper 1812, Federal Reserve Bank of Dallas. https://www.dallasfed.org/-/media/documents/research/papers/2018/wp1812.pdf. Accessed 12 March 2020.

Domaç, I., & Yücel, E. (2005). What triggers inflation in emerging market economies? Review of World Economics / Weltwirtschaftliches Archiv , 141 (1), 141–164.

Dovern, J., Feldkircher, M., & Huber, F. (2016). Does joint modelling of the world economy pay off? Evaluating global forecasts from a Bayesian GVAR. Journal of Economic Dynamics and Control , 70 , 86–100.

Eickmeier, S., & Ng, T. (2015). How do us credit supply shocks propagate internationally? a GVAR approach. European Economic Review , 74 , 128–145.

European Central Bank. (2017a). Annual Report 2017. ECB European Central Bank, Frankfurt. https://www.ecb.europa.eu/pub/annual/html/ar2017.en.html#IDofChapter1 . Accessed 12 March 2020.

European Central Bank. (2017b). Domestic and global drivers of inflation in the euro area. ECB Economic Bulletin 2017 , 4 , 72–96. https://www.ecb.europa.eu/pub/pdf/other/ebart201704_01.en.pdf .

Feldkircher, M., Gruber, T, & Huber, F. (2020a). International Effects of a Compression of Euro Area Yield Curves. Journal of Banking & Finance , 113 (April), 1–14.

Feldkircher, M., & Huber, F. (2016). The international transmission of US shocks – Evidence from Bayesian global vector autoregressions. European Economic Review , 81 (C), 167–188.

Feldkircher, M., Huber, F., & Moder, I. (2016). Modeling the evolution of monetary policy rules in CESEE. Focus on European Economic Integration , 2016 (1), 8–27.

Feldkircher, M., Huber, F., & Pfarrhofer, M. (2020b). Factor Augmented Vector Autoregressions, Panel VARS, and Global VARs. In Fuleky, p. (Ed.), Macroeconomic forecasting in the era of big data. Advanced studies in theoretical and applied econometrics. Vol. 52 Springer International Publishing Basel (pp. 65–93).

Feldkircher, M., & Siklos, P.L. (2019). Global inflation dynamics and inflation expectations. International Review of Economics & Finance , 64 (C), 217–241.

Galesi, A., & Lombardi, M.J. (2009). External shocks and international inflation linkages: a global VAR analysis. https://ideas.repec.org/p/ecb/ecbwps/20091062.html Working paper series 1062, European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1062.pdf. . Accessed 12 March 2020.

Galesi, A., & Sgherri, S. (2009). Regional financial spillovers across Europe; A Global VAR analysis. IMF Working Papers 09/23 International Monetary Fund. https://ideas.repec.org/p/ecb/ecbwps/20091062.html . Accessed 12 March 2020.

George, E.I., & McCulloch, R. (1993). Variable selection via Gibbs sampling. Journal of the American Statistical Association , 88 (423), 881–889.

George, E.I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics , 142 (1), 553–580.

Georgiadis, G. (2015). Examining asymmetries in the transmission of monetary policy in the euro area: Evidence from a mixed cross-section global VAR model. European Economic Review , 75 (C), 195–215.

Gnan, E., & Valderrama, M.T. (2006). Globalization, inflation and monetary policy. Monetary Policy & the Economy , 4 , 37–54.

Hofmann, B., & Bogdanova, B. (2012). Taylor rules and monetary policy: A global “Deviation”? BIS Quarterly Review , 2012 (Sep), 37–49.

Huang, Y., Wang, X., & Hua, X. (2010). What determines China’s inflation? China Economic Journal , 3 (1), 69–86.

Huber, F. (2016). Density forecasting using bayesian global vector autoregressions with stochastic volatility. International Journal of Forecasting , 32 (3), 818–837.

IMF. (2020). IMF database. International Monetary Fund, Washington, DC. https://www.imf.org/en/Data . Accessed 11 March 2020.

Jahan, S. (2017). Inflation targeting: Holding the line. In: International Monetary Fund (Ed.), Back to Basics. Economic Concepts Explained. IMF, Washington, DC, pp. 72–73. https://www.imf.org/external/pubs/ft/fandd/basics/pdf/jahpan-inflation-targeting.pdf .

Kamber, G., & Wong, B. (2020). Global factors and trend inflation. Journal of International Economics , 122 , 103265.

Kočenda, E., & Varga, B. (2018). The impact of monetary strategies on inflation persistence. International Journal of Central Banking , 14 (4), 229–274.

Krippner, L. (2013). Measuring the stance of monetary policy in zero lower bound environments. Economics Letters , 118 (1), 135–138.

Lanne, M., & Nyberg, H. (2016). Generalized forecast error variance decomposition for linear and nonlinear multivariate models. Oxford Bulletin of Economics and Statistics , 78 (4), 595–603.

Markov, N. (2015). A regime switching model for the european central bank. In Barnett, W. A., & Jawadi, F (Eds.) Monetary policy in the context of the financial crisis: new challenges and lessons Vol 24 of international symposia in economic theory and econometrics. Emerald publishing ltd, pp. 267–337 , https://ideas.repec.org/h/eme/isetez/s1571-038620150000024020.html . Accessed 13 march (p. 2020).

Mohanty, D., & John, J. (2015). Determinants of inflation in india. Journal of Asian Economics , 36 (C), 86–96.

Mumtaz, H., & Surico, P. (2012). Evolving international inflation dynamics: World and country-specific factors. Journal of the European Economic Association , 10 (4), 716–734.

Pain, N., Koske, I., & Sollie, M. (2006). Globalisation and Inflation in the OECD Economies. OECD Economics Department Working Papers 524, OECD Publishing. https://ideas.repec.org/p/oec/ecoaaa/524-en.html . Accessed 13 March 2020.

Pesaran, H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters , 58 (1), 17–29.

Pesaran, M.H., Schuermann, T., & Weiner, S.M. (2004). Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model. Journal of Business and Economic Statistics , 22 (2), 129–162.

Primiceri, G. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies , 72 (3), 821–852.

Svensson, L.E. (1999). Inflation targeting as a monetary policy rule. Journal of Monetary Economics , 43 (3), 607–654.

Taylor, J.B. (2007). Globalization and monetary policy: Missions impossible National Bureau of Economic Research, International dimensions of monetary policy. NBER chapters. NBER, pp. 609–624 . https://ideas.repec.org/h/nbr/nberch/0787.html . Accessed 13 March (p. 2020).

Taylor, J.B. (2014). The Federal Reserve In a globalized world economy. Globalization and Monetary Policy Institute Working Paper 200, Federal Reserve Bank of Dallas, https://ideas.repec.org/p/fip/feddgw/200.html . Accessed 13 March 2020.

World Input Output Database. (2016). World Input Output Database, Release 2016. http://www.wiod.org/home . Accessed 12 March 2020.

Download references

Author information

Authors and affiliations.

Oesterreichische Nationalbank (OeNB), Otto-Wagner-Platz 3, 1090, Vienna, Austria

Martin Feldkircher

Vienna School of International Studies (DA), Favoritenstraße 15a, 1040, Vienna, Austria

Department of Economics, Institute for International Economics, Vienna University of Economics and Business (WU), Welthandelsplatz 1, 1020, Vienna, Austria

Gabriele Tondl

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Gabriele Tondl .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This paper was written while Gabriele Tondl was a visiting fellow at the Austrian National Bank. She also gratefully acknowledges funding under the Research Contract 2016 of the Anniversary Fund of the Vienna University of Economics and Business. The opinions expressed in this paper are those of the authors and do not necessarily reflect the official viewpoint of the Austrian National Bank or the Eurosystem.

Rights and permissions

Reprints and permissions

About this article

Feldkircher, M., Tondl, G. Global Factors Driving Inflation and Monetary Policy: A Global VAR Assessment. Int Adv Econ Res 26 , 225–247 (2020). https://doi.org/10.1007/s11294-020-09792-2

Download citation

Published : 21 August 2020

Issue Date : August 2020

DOI : https://doi.org/10.1007/s11294-020-09792-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Monetary policy

JEL Classification

• Find a journal
• Publish with us
• Track your research

• Public Service That Makes a Difference ®

2022 • 22–4

Research department working papers, inflation levels and (in)attention.

Forming expectations is essential for optimal decision making and sits at the core of economic behavior. Will inflation expectations remain anchored in light of recent pandemic-related increases in inflation? That’s a central question in the current policy debate. The authors of this paper construct two different proxy measures of consumers’ inattention in order to explore evidence of inattention among U.S. consumers. This paper is the first in the literature to examine direct measures of consumer inattention to inflation over a long period of time, rather than inferring inattention from the behavior of inflation forecasts as done in previous works—an important examination since inflation expectations are key determinants of economic activity.

Cancel the RRB Cancellation

Monetary Policy  |  Retirement Saving and Income  |  August 22, 2024

The Study in Brief

• Two years ago, the federal government ceased issuing Real Return Bonds, which are a valuable tool for Canadian savers to protect themselves from inflation.
• The government cited weak demand for RRBs as justifying the cancellation, without acknowledging that its approach to RRBs itself promoted a thin market that lessened the attractiveness of RRBs to potential investors.
• A survey of 13 institutional investors suggests that an improved RRB program, with larger issues and more diversity of terms, would help the federal government finance its debt and give Canadians better access to an asset that many would like to hold.

Press release

We thank Jeremy Kronick, David Dodge, Charles DeLand, Mawakina Bafale, Steve Ambler, Bob Baldwin, Pierre Duguay, Ashwin Gopwani, David Laidler, Angelo Melino, Eric Monteiro, John Murray, James Pierlot, and Brent Simmons, as well as members of the C.D. Howe Institute’s Fiscal and Tax Competitiveness and Pension Policy Councils, anonymous reviewers, and the respondents to our survey of institutional investors for their contributions to this report. We are responsible for any errors and for the conclusions reached.

Introduction and Overview

The federal government stopped issuing Real Return Bonds (RRBs) in 2022.The cancellation of the RRB program came as a surprise; the government’s announcement said nothing about the value of inflation-indexed securities in capital markets, either as symbols of governments’ commitment to low inflation or as tools to understand inflation expectations and value-indexed obligations such as pensions. The announcement justified the decision by referring to weak demand and illiquid markets. It did not acknowledge that the government’s management of the RRB program – notably the small amounts issued and lack of diversity in maturities – made for a thin market, in which illiquidity discouraged investors from buying and holding RRBs.

Indexed debt fills an important gap in financial markets. The government’s cancellation of the RRB program means that Canadian savers will have less access to a uniquely valuable tool to protect themselves from inflation. The pension funds and other institutions that invest on individual Canadians’ behalf will lose a key tool to help them deliver on their promises.

For most of the decade prior to their cancellation, demand for RRBs suffered not only from a thin market, but also because inflation tended to be below the Bank of Canada’s 2 percent target and the federal government was issuing relatively little debt of any kind. The recent surge of inflation and explosion of federal debt has changed that environment, making this a propitious time to launch a new and improved RRB program.

Prompted by the government’s claim that the program was cancelled because of weak demand, the C.D. Howe Institute surveyed institutional investors with a total of $2.6 trillion of assets under management, and substantial holdings of RRBs, asking them if they thought demand for RRBs was low and, if so, why they thought it was low. Their answers affirm the value of RRBs in principle, and attribute the poor functioning of Canada’s RRB market to its thinness, with small issues and lack of diversity in maturities particularly cited. Not one of the 13 institutional investors surveyed supported the government’s decision to cease issuing the bonds. The federal government should resume issuing RRBs – in greater amounts and with more diversity of terms than before.

What Are Real Return Bonds and Why Are They Beneficial for Canadians?

Surging inflation recently reminded Canadian savers and lenders of the threat of being repaid in currency that has unexpectedly lost purchasing power. This threat can prevent mutually beneficial exchanges by creating a gap between the interest rate lenders want and the rate borrowers are willing to pay.

Bonds like RRBs that are indexed to the price level – often called “linkers” because their returns are linked to prices – address this problem, ensuring that their principal retains its real purchasing power.  Like the indexed bonds of other countries that issue them, the principal of the federal government’s RRBs changes with the Consumer Price Index (CPI), based on the difference between the CPI at the time of issue and the CPI at a reference period (typically three months before the adjustment date). The coupon payment is the stated coupon rate multiplied by the adjusted principal. The adjusted principal is repaid at maturity. 

Despite inflation protection’s attractiveness to lenders, private indexed debt of this kind is rare. Individuals and businesses do not control inflation, and notwithstanding inflation’s erosion of the real value of principal, it can hurt the financial health of private borrowers in other ways—through its interaction with taxes, for example. For a private borrower, promising to repay a purchasing-power-adjusted loan feels, and often is, risky.

Public-sector borrowers are better placed to issue indexed debt. Most national governments powerfully influence inflation through their central banks, either because they control monetary policy directly, or because they set mandates for their operationally independent central banks, as Canada does through the inflation targets jointly set by Parliament and the Bank of Canada. Importantly, changes in the price level tend to affect tax revenues one-for-one.  Many countries began issuing indexed bonds after the 1970s inflation surge, including the UK (1981), Australia (1985), Sweden (1994), New Zealand (1995), and the United States (1997).

Canada issued its first RRBs in 1991 with a $700 million offering. Those bonds, like all their successors, had a 30-year maturity. Their coupon rate, their effective interest rate after adjustment for inflation, was 4.25 percent.

The Benefits of Canada’s RRB Program

The primary goal of federal debt management in 1991, as expressed in its Debt Operations Report (DOR) for the 1991/92 fiscal year (Canada 1992), was to raise funds at the minimum long-term cost and diversify the debt program cost-effectively.

At that time, keeping the cost of borrowing down was a critical issue for the federal government. The government had run continuous deficits since the 1960s, and the 1990 recession sent the debt and interest costs into uncharted territory, with its gross debt charges close to 30 percent of total expenses. Other debt-management changes around the same time included issuing a 30-year Government of Canada nominal bond, increasing the size of benchmark bond issues, establishing regular dates for issuing debt, and replacing syndicated issues with auctions.  DORs and Debt Management Reports (DMRs) in the mid-1990s added the goal of developing – later maintaining – efficient capital markets, notably for government debt, through the above measures, as well as developing a diverse investor base (see, for example, Canada 1995 and Canada 1997).

RRBs offer unique protection against inflation, a serious threat to financial security. The expense and complication of selling bonds directly to individuals discouraged the government from retailing RRBs directly to individual investors, but people who save through pensions with partial or full inflation protection, purchase an indexed annuity, or receive long-term disability benefits have indirect access to RRBs.

Savers, via pension funds, insurers and other institutional investors who manage their savings, can buy other assets that appreciate in value and/or yield revenue streams correlated with price increases, such as real estate and infrastructure. Unlike the precise link between prices and the principal value of RRBs, however, the correlation between the prices of those assets and prices generally is imperfect. The recent inflation surge, for instance, accompanied a drop in revenues from commercial leases, making commercial real estate a poor hedge this time. Additionally, the stock of infrastructure investments available to private investors in Canada – such as utilities, airports, toll bridges, and highways – is limited.

Institutional investors can, and do, buy infrastructure in other countries, but that provides revenues correlated with those countries’ inflation rates, which may not perfectly correlate with Canadian inflation, and exposes the investors and the savers whose money they manage to exchange-rate risk. Other countries’ governments, notably the US government, also issue inflation-linked bonds, as we discuss below, but those are not readily available to retail investors, and owning them exposes Canadian savers not only to exchange-rate risk but to interest-rate risk as well.

RRBs are the only market instruments that offer full protection against Canadian inflation. It is reasonable to think that a thicker market in RRBs would improve pricing and promote the development of retail products that offer inflation protection. Intermediaries need not hold RRBs directly to benefit from the existence of a market for them, since the yield on RRBs allows valuations of indexed liabilities that guide the funding decisions of plan managers (CIA 2023; ACPM 2023).  The discontinuance of RRBs will make it harder for Canadians and institutional investors to hedge against inflation.

Issuance of RRBs Compared with Nominal Bonds

Unlike many other countries mentioned, the Canadian federal government has issued relatively small amounts of indexed debt. Balanced budgets under Prime Minister Jean Chretien and Finance Minister Paul Martin eliminated the federal government’s net financing requirement in 1997/98, resulting in fewer bonds overall and fewer RRBs. Although RRB issues picked up somewhat when the federal government again began borrowing at the time of the 2008 - 2009 financial crisis and recession, RRB issuance never much exceeded $2.2 billion annually. In 2019/20, issuance fell to $1.8 billion, and in 2020/21 and 2022/22, it further dropped to $1.4 billion despite federal borrowing’s explosion and the maturation of the first RRB issue (Table 1).

Immediately before the program’s cancellation, annual issuance was about half a percent of gross annual bond supply, resulting in a relatively small float of RRBs. The federal government’s inflation-linked debt outstanding ranks among the lowest of other countries, both in absolute value and as a percentage of total debt securities. Continued heavy government borrowing with no new issues of RRBs means that the ratio of RRBs to total bonds outstanding will continue to fall (Table 2).

How Has the RRB Program Performed and Has it Met Its Goals?

How have RRBs performed as a financing instrument for the government and as an asset for investors and capital markets? A good place to start on this question is a review of their yields relative to yields on the 30-year nominal Government of Canada bonds the government began issuing around the same time.  Figure 1 shows the yields on both from the inception of the RRB program to the present day, as well as the spread between them. 

Since the difference between the two bonds is that the nominal bond offers no protection against inflation while the RRB offers protection against inflation, in a liquid market with prices reflecting arm’s-length trading, expectations of inflation should play a major role in determining the spread between them.

The spread between the yields on nominal and RRBs since the early 1990s is consistent with a story of increasing confidence in the Bank of Canada’s ability to achieve and maintain low inflation. Initially, investors were skeptical. After decades of high inflation, some doubted that monetary policy could engineer low inflation. Some who thought it possible doubted that politics would allow it. The disagreement between then-Bank of Canada Governor John Crow and the Liberal government that took office in 1993 regarding inflation targets led to the replacement of Crow, which raised concerns that inflation might be higher under the new government (Laidler and Robson 2004, p. 84). The spread between yields on the two types of bonds stayed around 4 percent until 1995. By then, actual experience of low inflation and the new government’s agreement to an ongoing inflation target of 2 percent past the end of 1995 changed minds. The spread fell to around 2 percent and the 12-month moving average of the spread has been within one percentage point of 2 percent ever since.

Inflation expectations have not been exactly 2 percent since the end of 1995 so, next, we look at the spread between nominal bonds and RRBs in the light of long-term inflation forecasts. Figure 2 compares the quarterly yield spread with the average of all the individual predictions collected by Consensus Economics for CPI inflation 6 to 10 years ahead in Canada. 

The behaviour of the quarterly nominal-RRB spread compared to inflation forecasts in the early 1990s is consistent with the story of low initial confidence in economists’ inflation forecasts, followed by improvement. Initially, the spread was over 1 percentage point above inflation expectations, sometimes exceeding 2 points. After the first three years of RRB issuance, it declined closer to target inflation. From late 1997 until the mid-2010s, the gap between the spread and expected inflation was usually mildly positive. As we explain in the next section, nothing is remarkable about that. What does seem peculiar is that the gap reversed after mid-2014. The yield spread was 0.5 percentage points higher than forecasted inflation on average until mid-2014, then 0.3 points lower on average after that. As explained below, the thinning of the market by inadequate RRB supply likely explains relatively high RRB yields and the reversal of the gap.

Cancellation: Inadequate Supply Hurting Liquidity and Market Reaction

The federal government’s last issue of RRBs was in the spring of 2022. In its Fall Economic Statement in November 2022, the government announced the program’s cancellation. The government justified the cancellation as a response to low market demand.

The termination surprised institutional investors. They had noted low demand, but explained it with reference to the thin market resulting from low supply in general and small benchmark sizes in particular. Respondents to government consultations in 2019 attributed the declining demand for RRBs to three main factors: the relatively low liquidity of the RRB market, diminished inflation risk, which reduced the necessity for inflation protection, and the generally low yield environment, which compelled investors to seek higher-yielding asset classes that also provide some inflation protection. None had suggested that issuance was excessive (Canada 2019a).

The 2019 consultations noted that holders of RRBs valued the inflation protection they offer – some saying they were uniquely useful in that regard, others saying that the indexed debt of other governments and assets such as real estate were also attractive. The summary also reported that when asked about longer-term trends in demand and pricing, pension funds and insurers – institutions likelier to want inflation protection to match their liabilities – saw illiquidity, lower inflation and generally low yields as making RRBs less attractive than other assets offering inflation protection (Canada 2019a).

With no new issuance, the existing stock of RRBs will shrink as the bonds mature: down 35 percent within the next 12 years, down 64 percent within the next 20 years, and eliminated in 30 years. Other domestic issuers are unlikely to fill the gap left by the federal government, which, for the reasons already noted, can issue RRBs with more confidence than provinces and far more confidence than private borrowers. The last provincial issuance of RRBs dates to 2008, while the corporate inflation-linked bond market is almost non-existent (Cook 2022). Further thinning of the market for RRBs will likely adversely affect the availability and pricing of other indexed products: indexed annuities, for example, will likely become more expensive (Normandin Beaudry 2024).

On the subject of liquidity, or its absence, the summary of the 2019 consultations reported that all participants said secondary market liquidity for RRBs was poor, and that illiquidity deterred clients and dealers from holding RRBs and discouraged the development of derivatives (Canada 2019a). A reasonable inference from these comments, as from similar comments in previous consultations, is that a larger float of RRBs could improve liquidity, demand and pricing.  Paradoxically, that report concluded by saying that the government would reduce RRB issuance from $1.8 billion to $1.4 billion annually.

The Bank of Canada’s Fall 2022 Debt Management Strategy Consultations registered another go-round of this vicious circle of inadequate supply hurting liquidity and thereby suppressing demand: “…participants noted that there continues to be little demand for this product. This low demand reflects RRBs’ general lack of liquidity, as well as small stock and benchmark sizes.” It also noted that “Canadian investors seeking inflation protection are relying on other instruments such as infrastructure projects and US Treasury Inflation-Protection Securities (TIPS)” (Bank of Canada 2022).

The Root of All Problems: Inadequate RRB Supply

Bond yields vary inversely with bond prices. As noted already, since the difference between nominal bonds and RRBs is inflation protection, expected inflation should play a major role in determining the difference in their yields. Straightforwardly then, expected inflation should play a major role in determining the difference in their prices. In addition, there should be a premium on RRBs – their price should also be higher and their yield lower – or, coming at it the other way, there should be a discount on nominal bonds – their price should be lower and their yield higher – reflecting the value of protection against unexpected inflation. It makes sense that the gap between the nominal/RRB yield spread and forecasted inflation would typically be positive; it means that, if inflation is typically in line with expectations, the government should be able to reduce its overall debt-service costs by issuing RRBs. 

Investors’ willingness to pay for inflation protection was probably greatest in the years immediately following the first issue of RRBs. The gap between the nominal/RRB yield spread and forecasted inflation was relatively large in the early 1990s when experience had not yet proved that the 2 percent inflation target was technically achievable and politically supportable. Investors’ willingness to pay for inflation protection was probably lower during the 2010s, when inflation tended to undershoot expectations and the Bank of Canada’s target.

But it is hard to understand why investors’ willingness to pay for inflation protection would at other times be zero or even negative as it has been since 2014 (Figure 2). Why would bondholders be indifferent between nominal bonds and RRBs, valuing inflation protection at nothing? More dramatically, why would people be able to buy the asset with inflation protection at a discount? Although the spread between the yields on US nominal bonds and TIPS dropped below two percent around the middle of the last decade and during the COVID pandemic, it has averaged around 2.3 percent since July of 2021, well above the 1.8 percent spread between the yields on Canadian long bonds and RRBs over that time. 

To make sense of a gap between a nominal/RRB yield spread and forecasted inflation of zero or less than zero, we need something that makes RRBs trade at a discount. The prime suspect is illiquidity. Before the explosion of federal debt in 2020/21, which lowered the ratio of trading to outstanding in all Government of Canada bonds, the average turnover of long-term nominal bonds was in the 5-10 percent range, while the average turnover of RRBs was less than 1 percent (Canada 2019b, Chart 12). Lack of a ready market would make holders or potential holders of RRBs want a lower price – that is, a higher yield – to compensate them for the risk of not being able to sell an RRB as quickly or at as narrow a bid-ask spread as they could sell a nominal bond.

Scarcity typically raises prices, but an asset so rare that demand for it does not develop can see its value drop. Financial assets often show that supply fosters demand, while lack of supply inhibits it. For instance, if the Canadian government had issued only 100 $1 coins, the “Loonie”, its actual value to holders would have been less than its face value due to unfamiliarity and lack of infrastructure investment, with people continuing to use $1 banknotes.  Critical mass matters in government bonds, as ensuring ample supply of certain bonds (benchmark issues) promotes liquid markets, raising prices and lowering yields. 

The federal government, consistent with its early objective of diversifying its investor base, had plans to broaden the market for RRBs. In the mid-1990s, the government set aside two $100 million tranches of RRBs for a syndicate of investment dealers to repackage as strips – separating principal from interest for retail retirement saving plans (Miller 1997). However, the federal government’s falling net financing requirement in the late 1990s coincided with a general decline in bond market liquidity after the Asian financial crisis of 1998. The government took measures to maintain liquidity for benchmark issues, such as reducing the frequency of auctions for long-term bonds and instituting a buyback program for less liquid issues (Canada 1999, 2000). This environment discouraged the issue of RRBs and other efforts to encourage wider holding and trading of RRBs. The thinner market depressed RRB prices, and the gap between the nominal/RRB yield spread and forecasted inflation turned negative from the end of 1998 to the start of 2000 and briefly again at the end of 2001.

Although the gap between the yields on nominal bonds and RRBs was positive on average for the first 15 years of the 2000s, the market for RRBs has always tended to be less liquid than the market for nominal bonds. The buyers of most nominal bonds at auction are primary dealers – market-makers who buy the bonds to trade, not long-term investors. Over the four fiscal years from 2002/03 to 2005/06, for example, primary dealers bought more than 90 percent of nominal bonds at auction, and customers – likelier to be long-term investors – rarely bought more than 5 percent. Long-term investors were far more active in auctions for RRBs. Over that same 2002/03-to-2005/06 period, the split between purchases of RRBs at auction by primary dealers and customers was closer to 50:50.

Coverage ratios – total bids relative to auction amount – were higher for RRBs than for nominal bonds during this period, all the more notable because RRB auctions limited any single buyer to 25 percent of the total available. That indicates enthusiasm from long-term investors, whose tendency to buy and hold the limited amounts on offer made traders less enthusiastic about RRBs.  These attributes contribute to reducing the liquidity of the secondary market for RRBs. A 2011 article in Risk magazine highlighted the view of Canadian RRB investors that new issues were inadequate to meet demand (Pengelly 2011).

The problem of illiquidity was central to a C.D. Howe Institute report published in 2012 (Bergevin and Robson 2012), which noted that RRB auctions featured higher coverage ratios and greater investor interest. It also emphasized that the attractiveness of RRBs to long-term investors meant that RRBs had relatively low turnover – with much of each new issue merely matching the appetite of existing RRB holders to reinvest their coupon payments. That report recommended that the federal government issue more RRBs and add different maturities to foster a more robust market, including derivatives and other inflation-indexed products, and reduce its debt servicing costs.

Notwithstanding these arguments, the federal government continued to issue relatively small amounts (averaging about $550 million per quarter) of RRBs and stayed exclusively with 30-year maturities. Moreover, the government of Prime Minister Stephen Harper and Finance Minister Jim Flaherty eliminated the federal government’s post-2008 financing requirement by 2012/13, and the government again became a net retirer of debt – which did not dictate a reduction in RRB issues particularly, but created headwinds for any advocates of expanding the stock of any particular bond. An additional negative for the RRB market was that inflation undershot the Bank of Canada’s 2 percent target more often than it overshot it over the decade after 2008. None of these circumstances promoted trading in RRBs.

Contrary to the government’s justification for cancelling the RRB program, the negative reaction from market participants at the time, as expressed in the Bank of Canada’s Fall 2023 Debt Management Strategy Consultations (Bank of Canada 2023), and the responses to our survey (detailed below) demonstrate that the potential demand for RRBs among long-term investors remains strong. The perceived lower investor interest outside that core group, and even within it, seems to be primarily due to inadequate supply and the resulting thin market.

Responses to the C.D. Howe Institute Survey

In an attempt to better understand the conflicting views – the desire for larger, more diverse RRB issues from many RRB investors and other bond-market experts, versus the evident desire on the part of the government to limit RRBs issues and ultimately to do away with them – the C.D. Howe Institute conducted its own survey of institutional investors during 2023.

The Institute’s survey (reproduced in the Appendix) asked potential respondents to provide background information, notably on their assets under management (AUM), whether they held RRBs, and why they did or did not. It asked whether respondents supported the government’s decision to cease issuing RRBs. It asked if respondents agreed that demand for RRBs is low and that liquidity in the RRB market is inadequate. It further asked respondents who said that RRB demand is low to identify reasons for low demand. It asked respondents how likely they would be to buy RRBs in the future if the government did or did not resume issuing them, and asked about potential changes to the RRB program that might increase demand and liquidity. Finally, it asked what other assets providing protection against inflation respondents held, and whether greater availability of RRBs might reduce their demand for those alternatives. A snapshot of responses is shown in Figure 3.

Thirteen institutional investors responded to the Institute’s survey – four jointly governed employee pension funds, five public investment managers, one large insurer, and three large asset management firms. All manage pension assets. The median AUM of the 13 was around $200 billion, and the total AUM of the 13 was more than $2.4 trillion. The aggregate amount of RRBs held by the 13 was about $19 billion, or almost 40 percent of the stock of outstanding RRBs.

The respondents indicated that they held RRBs to match the liabilities of their clients, both as expressed by clients (mainly other pension funds and insurers) or in accordance with their managers’ judgements. They mentioned the unique value of RRBs in managing the exposure of pensions with indexed liabilities to Canadian inflation. Twelve of the 13 said that recent higher inflation in Canada had increased their interest in inflation-protected assets.

Among the respondents that did not hold RRBs and said why they did not, one indicated that the nature of its client’s liabilities made other inflation-linked assets more suitable, and the others indicated that the RRB market had problems, such as lack of liquidity and transparency, unattractive valuations, no commitment from the government and the Bank of Canada to support the market, and inflexible issuance.

None of the 13 supported the government’s decision to cease issuing RRBs. Twelve opposed it, and one had no opinion.

Asked if they agreed that demand for RRBs is low, four respondents said yes, eight said no, and one had no opinion. Those who agreed that demand is low, and who expressed opinions about why it is low, cited considerations similar to those just mentioned: lack of liquidity and lack of support for the program, as well as unattractive attributes of RRBs and the availability of alternatives.

Asked if they agreed that liquidity in the RRB market is inadequate, 12 respondents said yes and one said no. Those who agreed that liquidity is inadequate and expressed opinions about why it is inadequate cited the preference of purchasers to buy and hold the bonds, the predominance of long maturities (and associated inadequate derivatives), and lack of support for the market from the government and the Bank of Canada. One respondent pointed out that growth of demand for inflation-protected assets on the part of Canadian pension plans is relatively predictable, which should help the government calibrate the program to fulfill it, and support more frequent trading and a healthy derivatives market.

Responses to the question about their likely future appetite for RRBs differed markedly between the scenario where the government does not resume issuing them and the scenario where it does. In the event that the government does not issue more RRBs, three respondents said they were very likely to buy more, two said they were somewhat likely to buy more, and eight said they were unlikely to buy more. In the event that the government does resume issuing RRBs, eight said they were very likely to buy more, four said they were likely, and one said it was unlikely. In the event that the government does resume issuing, the total amount respondents indicated they would likely buy was $7.9 billion over the next three years.

When asked what changes they would recommend if the government resumed issuing RRBs, the most frequent answer – from 11 of the 13 respondents – was creating RRBs of different maturities. Eight of the respondents suggested issuing at more regular intervals, eight suggested that the government and/or the Bank of Canada should better support the market, and six suggested issuing larger amounts. One suggested issuing a bond with a principal amount that could not fall below par.

Respondents mentioned holding a variety of assets other than RRBs that provide protection against inflation: other sovereign indexed bonds – mostly US TIPS – real estate, utilities, infrastructure, commodities, leases and inflation swaps. Four of the respondents said greater availability of RRBs would induce them to hold less of these alternatives, with two mentioning holding less US TIPS particularly. Five said they would hold the same amount of these alternatives. Three did not express an opinion one way or the other, and one underlined the importance of the design and market attributes of RRBs in a new program for making such a decision.

Two investors added comments to the survey mentioning their desire for provincial RRBs. Several respondents also noted that it is peculiar for the federal government, having recently raised concerns about Canadian pension funds investing relatively little in Canada, to stop issuing such a valuable domestic asset, and thus indirectly encourage more investment abroad.

Discussion and Recommendations

Reinforcing the Credibility of Inflation Targeting

Resuming and expanding the RRB program would strengthen the credibility of the government’s commitment to maintaining 2 percent inflation. A straightforward reason for this is that more RRBs would visibly diminish the fiscal advantages of higher inflation. From the start, signaling a commitment to controlling inflation was a clear objective of the RRB program (Johnson 1998). The government’s decision to stop issuing RRBs led to suspicions that it anticipated consistently higher inflation in the future (see Cook 2022, for instance).

More subtly, introducing RRBs with various maturities could assist the Bank of Canada in achieving its 2 percent inflation target (Bergevin and Robson 2012). An additional objective of the RRB program at the start was to create a market-based measure of expected inflation (Johnson 1998). Offering more RRBs with maturities that align with those of other nominal bonds would allow the Bank of Canada to compare nominal and real yields across a broader range of maturities. This comparison would provide more accurate information about inflation expectations than current methods such as surveys or the analysis of existing bonds (Smith 2009). It would also enable the Bank to respond more swiftly to shifts in sentiment or actions that could undermine confidence in the inflation target.

Reinforcing the government’s commitment to 2 percent inflation might appear a drawback for some. Opponents of a stronger commitment might see greater inflation as a useful tool for a fiscally troubled government, on the grounds that devaluing existing nominal debt and taxing a larger inflation component of investment income and capital gains is less economically damaging than alternatives such as higher marginal tax rates, cuts in spending or outright debt defaults. A country must be in deep trouble, however, to make inflation – which itself has major and pervasive economic and social costs – a relatively attractive option (Bergevin and Robson 2012). Canada is not in such trouble and has plenty of fiscal options to keep itself out of it.

Since containing borrowing costs is a central and appropriate goal of the government’s debt management, we emphasize that the gap between the nominal bond/RRB yield spread and inflation in Figure 2 refers to expected, not realized, inflation. There is evidence that recent actual inflation influences people’s expectations, and investors’ expectations may differ from those of professional forecasters. From 2012 to 2021, realized inflation came in below target on average, and below inflation expectations. The comparison with forecasted inflation overstates the extra amount the government would have had to pay bondholders; in other words, the government saved money from inflation being below target, making the RRBs a better deal for them after the fact. The more recent surge of inflation above the 2 percent target had the opposite effect. Over the three fiscal years from 2021/22 to 2023/24, we calculate that inflation above 2 percent cumulatively added more than $5 billion to federal expenses.

We do not know what role higher inflation adjustments on RRBs played in the government’s decisions to cancel their issuance. The government may dislike the inflation adjustment showing up in their debt servicing costs. However, the government has a natural hedge on the revenue side, since revenues tend to rise and fall one-to-one with inflation. It also has massively higher exposure to inflation on the expense side, including through the indexed pensions of federal employees. The additional exposure from issuing more RRBs would be small by comparison. Since the adverse surprises on debt servicing costs only arise if inflation exceeds the government’s target, RRBs are valuable signs of the government’s commitment to inflation control.

Promoting Market Liquidity

The government’s citation of promoting liquidity of benchmark bond issues as a reason to stop issuing RRBs is odd, given the recent explosion in the federal government’s gross debt issuance and the forecast for continued high financing requirements throughout the fiscal plan. With both gross and net bonds outstanding expected to grow, there will be ample supply of 30-year and 10-year nominal bonds.

Moreover, the government has options in issuing new RRBs, particularly a potential 10-year issue, to further mitigate the risk that introducing the new bonds will cause indigestion in the market. It can vary the pace of issue, depending on feedback from buyers and dealers, experiment with multiple-price auctions similar to those used for nominal bonds, and launch new RRBs through syndication to test demand without risking a failed auction.

A more liquid RRB market with a 10-year and/or other shorter maturities could promote the development of derivatives and more inflation-linked products, notably price-indexed annuities and vehicles providing coverage for disability and long-term care.  As noted already, the value of inflation-linked bonds as a share of total debt securities in many other countries is much larger than in Canada.

RRBs are a valuable asset for Canadian savers and a valuable component of Canadian capital markets. The federal government’s decision to cease issuing RRBs will mean that Canadians, and the institutions that invest on behalf of many of them, will have less access to an asset that offers unique inflation protection.

Whatever the experience of below-target inflation and the federal government’s limited financing requirements over the decade before the COVID pandemic, subsequent inflation and the explosion of federal debt issues have changed the environment for RRBs.

The federal government should resume issuing RRBs – in greater amounts and with more diversity of terms than before.

Appendix: The C.D. Howe Institute’s Survey of Major Canadian Institutional Investors that Hold, or Might in Future Hold, RRBs

The federal government announced in its 2022 Fall Economic Statement that it would cease issuing Real Return Bonds (RRBs), citing lack of demand and liquidity. The following questions are intended to elicit information from major Canadian institutional investors that currently hold RRBs or might hold them in the future about their holdings of RRBs, if any, and about the market for RRBs and the government’s decision. Responses will be aggregated without identifying individual responders.

1. What were your organization’s assets under management (AUM) as of December 31st 2022 (Check one; please indicate if reporting date other than December 31st 2022)?

• $0 to $10 billion
• $10 to $50 billion
• $50 to $100 billion
• $100 to $300 billion
• More than $300 billion

2. Does your organization currently hold RRBs? (Check one)

2a. If you answered “yes” to question 2, what is the size of your RRB holding, in $ millions:

__________________

3. Has the recent higher inflation in Canada and elsewhere led to an increase in your prospective appetite for inflation-protected assets?

4. If your organization holds RRBs, why does it hold them? (List as many reasons, and use as much space for your answer, as you wish.)

_________________________________________________________________

5. If your organization does not hold RRBs, why does it not hold them? (List as many reasons, and use as much space for your answer, as you wish.)

6. Do you support the federal government’s decision to cease issuing RRBs? (Check one)

7. Do you agree that market demand for RRBs is low? (Check one)

7a. If you answered “yes” to question 7, to what do you attribute low demand for RRBs? (Select as many as appropriate):

- lack of liquidity

- unattractive pricing

- unsuitable design/attributes of RRBs

- lack of federal government and/or Bank of Canada support for the RRB market

- availability of alternatives

- other (please specify)

_________________________

8. Do you agree that liquidity in the RRB market is inadequate? (Check one)

8a. If you answered “yes” to question 8, to what do you attribute the inadequate liquidity for RRBs? (Select as many as appropriate):

- purchasers prefer to buy and hold

- design/attributes of RRBs make them unsuitable for trading

- predominance of long maturities among existing RRBs

- lack of federal and/or Bank of Canada support for the RRB market

9. If the federal government does not issue more RRBs, how likely is it that you would buy RRBs in the future?

• Very likely
• Somewhat likely

10. If the federal government resumes issuing RRBs, how likely is it that you would buy RRBs in the future?

10a. If you answered “yes” to question 10, what amount of RRBs, in $ millions, would you anticipate buying over the next three years?

___________________

11. If the federal government were to resume issuing RRBs, what measures would you recommend to increase demand for them and liquidity in the RRB market (tick all that apply):

• issue larger amounts
• issue at more regular intervals
• structure RRBs differently
• create RRBs of different maturities
• federal government and/or Bank of Canada support for the RRB market

other (please specify and/or elaborate any answer above)

12. Please list any other assets your organization currently holds that provide protection against inflation, whether the asset is formally indexed to prices (such as US Treasury Inflation-Protected Securities) or has characteristics that make it effective for that purpose (such as real estate).

13. Would greater availability of RRBs likely induce your organization to hold less of the other assets that provide protection against inflation? (Check one)

13a. If you answered “yes” to question 13, which assets would you expect your organization to hold less of?

Association of Canadian Pension Management. 2023. “Finance Canada’s Decision to Cease the Issuance of Real Return Bonds (RRBs) – Impact on Pension Benefit Security.” Open letter to the Hon. Chrystia Freeland, Minister of Finance. February 21.

Bank of Canada. 2022. “Fall 2022 Debt Management Strategy Consultations Summary.” November. Available at https://www.bankofcanada.ca/wp-content/uploads/2022/11/fall-2022-debt-management-strategy-consultations-summary.pdf .

Bank of Canada. 2023. “Fall 2023 Debt Management Strategy Consultations Summary.” December. Available at https://www.bankofcanada.ca/wp-content/uploads/2023/12/fall-2023-debt-management-strategy-consultations-summary.pdf

Bergevin, Philippe, and William B.P. Robson. 2012. More RRBs, Please! Why Ottawa Should Issue More Inflation-Linked Bonds. Commentary No. 363. Toronto: C.D. Howe Institute.

Canada. 1991. Debt Operations Report. Department of Finance Canada.

—. 1992. Debt Operations Report. Department of Finance Canada.

—. 1995. Debt Operations Report. Department of Finance Canada.

Canada. 1997. Debt Management Report. Department of Finance Canada.

—. 1999. Debt Management Report. Department of Finance Canada.

—. 2000. Debt Management Report. Department of Finance Canada.

Canada. 2019a. Government of Canada Real Return Bond Consultations Summary. Department of Finance and Bank of Canada.

Canada. 2019b. Debt Management Report 2018-2019. Department of Finance Canada.

—. 2023. Debt Management Report 2022-2023 . Department of Finance Canada.

Canada 2024. Budget 2024. Department of Finance Canada.

Canadian Institute of Actuaries. 2023. “Canada Real Return Bond Program.” Open letter to the Hon. Chrystia Freeland, Minister of Finance. January 16. Available at https://www.cia-ica.ca/publications/223019e/ .

Cook, Michael. 2022. “What you Need to Know about the Recent Real Return Bond Announcement.” CIBC Asset Management. November.

Garcia, Juan Angel, and Adrian van Rixtel. 2007. “Inflation-Linked Bonds from a Central Bank Perspective.” European Central Bank Occasional Paper Series , No 62.

Jevons, Stanley W. 1875. Money and Exchange. Appleton. New York.

Johnson, David. 1998. “Final Report: An Evaluation of Canada’s Real Return Bond Program, 1991-1997.” Treasury Evaluation Program. September.

Keynes, J. M. 1927. “Evidence to Committee on National Debt and Taxation.” Minutes of Evidence. London.

Laidler, David E.W. and William B.P. Robson. 2004. Two Percent Target: Canadian Monetary Policy Since 1991. Toronto: C.D. Howe Institute.

Laurin, Alexandre, and William B.P. Robson. 2020. “Under the Rug: The Pitfalls of an “Operating Balance” Approach for Reporting Federal Employee Pension Obligations.” E-Brief No. 309. Toronto. C.D. Howe Institute.

Manulife Investment Management. 2023. “Canada brings the curtain down on real return bonds.” June 16.

Miller, Robin. 1997. “Canada: Debt Management Policy and Operating Practices.” In Sundararajan, V., Peter Dattels, and Hans Blommestein, eds., Coordinating Public Debt and Monetary Management. Washington: International Monetary Fund.

Normandin Beaudry. 2024. “Annuity Purchases in Canada in 2023: The Enthusiasm Continues.” March.

Pengelly, Mark. 2011. “Canada pressed to develop inflation market.” Risk. March 4.

Shiller, Robert J. 2003. The Invention of Inflation-Indexed Bonds in Early America. Working Paper 10183. National Bureau of Economic Research. December.

Smith, Gregor W. 2009. The Missing Links: Better Measures of Inflation and Inflation Expectations in Canada. Commentary No. 287. Toronto: C.D. Howe Institute.

Twist Financial Corp. 2010. “Evaluation of the debt auction process: nominal bonds, real return bonds, treasury bills.” Ottawa: Department of Finance.

1 This advantage inspired suggestions for price-level-index bonds from Jevons (1875) and Keynes (1927) among others. Irving Fisher, a lifelong advocate for inflation-indexed bonds, co-founded the Rand-Kardex Co., which issued the first such bonds in 1925 (Shiller 2003). Protecting lenders from unanticipated inflation can make financial markets more complete and improve risk sharing (Garcia and Rixtel 2007).

2 Many other countries’ indexed bonds, such as US Treasury Inflation-Protected Securities (TIPS) cannot fall below par in the event of deflation. RRBs do not have this feature.

3 See, for example, the sensitivity analysis in the federal government’s 2024 budget (Canada 2024, pp. 385, 387-88).

4 Auctions replaced syndication in the issuing of RRBs in 1996 (Canada 1995).

5 The Canadian Institute of Actuaries estimates that about $1 trillion of assets back inflation-linked benefits for about 3 million Canadians (CIA 2023). Federal employee pension benefits would be better funded – and the total cost to taxpayers of the federal government’s pension promises would be better reported – if the federal government used the RRB yield in valuing its own liabilities (Laurin and Robson 2020).

6 “Of special note [in 1990/91] was the introduction of new 30-year Government of Canada bonds, introduced in response to investor demand and in line with the government’s term extension objective. These bonds quickly established themselves as a new benchmark for long bonds in the Canadian market” (Canada 1991).

7 The yields are from Statistics Canada Table: 10-10-0122-01. We use 12-month moving averages to smooth month-to-month fluctuations.

8 The future return on the RRB will be a function of inflation over the entire 30-year period until maturity. We are implicitly using the 6-to-10-year forecast for the long term, assuming that inflation expectations for years 6 through 30 would likely be the same, which would give the unobserved shorter-term expectations a small weight over the entire period’s expectation.

9 One post-cancellation analysis (Cook 2022) stated: “One reason cited for the decision was the lack of liquidity in the RRB market which deters investors. Liquidity is the ability to transact the amount you want at a fair bid/ask spread. RRBs were by no means liquid, but that was a result of the lack of supply relative to the demand.”

10 A bonus – and potentially a large one, since the government pays interest on far more nominal bonds than RRBs – would arise if a larger issue of RRBs reinforced confidence in the inflation target and therefore lowered the yield on nominal bonds (Bergevin and Robson 2012). Even if greater confidence reduced the direct saving from issuing more RRBs, the government would further reduce its total debt servicing cost.

11 The US Federal Reserve’s target for inflation is not specified in terms of the CPI, and is less formal than the Canadian inflation target, which is jointly set between the Bank of Canada and Parliament, so the comparison is not exact. But a spread between nominal bonds and linkers that is higher than the inflation target, as exists in the United States, is what one would expect in a well functioning debt market.

12 A $25 banknote existed for decades, but was rare and rarely used. It ceased being legal tender in 2021.

13 Increasing supply of certain bonds to improve liquidity and thereby raise prices – that is, reduce yields – is a constant theme of the federal government’s DORs and DMRs. To pick an example from the mid-1990s, when oversupply of government debt was depressing its price, and raising interest rates, generally, the 1995 DOR stated: “The government has pursued a strategy to improve liquidity in Canada’s bond market through larger benchmark bond sizes. The average size of the 21 Government of Canada bond auctions during the year was approximately $1,980 million, up about $135 million per issue from fiscal 1993/94. In March 1994, target sizes for benchmark issues were increased: for two-year bonds, to $4 to $6 billion; and for five-, ten-, and thirty-year bonds, to $6 to $9 billion. All of these targets were met during the 1994/95 fiscal year. Building upon the success of its continuing quarterly cycle of two-, five and ten-year auctions, the government issued thirty-year bonds each quarter” (Canada 1995).

14 See DMR 2006, especially Tables 5, 6 and 8.

15 As Bergevin and Robson (2012) noted, the introduction of TIPS in the United States supported the emergence of new financial products such as inflation futures, inflation swaps and inflation-linked benchmark indices. US pension funds subsequently expanded their offerings of inflation-linked investment plans and annuities (Garcia and Rixtel 2007).

This E-Brief is a publication of the C.D. Howe Institute.

William B.P. Robson is President and CEO of the C.D. Howe Institute.

Alexandre Laurin is Director of Research at the C.D. Howe Institute.

This E-Brief is available at www.cdhowe.org .

Permission is granted to reprint this text if the content is not altered and proper attribution is provided.

The views expressed here are those of author. The C.D. Howe Institute does not take corporate positions on policy matters.

Bill Robson took office as President and CEO of the C.D. Howe Institute in July 2006, after serving as the Institute’s Senior Vice President since 2003 and Director of Research from 2000 to 2003. He has written more than 270 monographs, articles, chapters and books on such subjects as government budgets, pensions, healthcare financing, inflation and currency issues.

Alexandre is the Director of Research and leads the fiscal policy program and the pension policy program at the C.D. Howe Institute. He joined the C.D. Howe Institute in 2008 and became Director of Research in 2014. From 1999 to 2008, Mr.

Related Research

Toolkit of Economic Indicators

Tell-tale Signals: A Customized Toolkit for Tracking the Economy

Strength in Diversity: What We Can Learn from BC’s Target-benefit Plans

Pension Roulette: The Risks and Consequences of Alberta Leaving the CPP