EFFECTS OF INFLATION EXPECTATIONS ON INFLATION

Abstract

We study the effects of professionals’ survey-based inflation expectations on inflation for a large number of 36 economies, using dynamic cross-country panel estimation of New-Keynesian Phillips curves. We find that inflation expectations have a significantly positive effect on inflation. We also find that the effect of inflation expectations on inflation is significantly larger when inflation is higher. This suggests that second-round effects via the effects of higher inflation expectations on inflation are more relevant in a high-inflation environment.

1. Introduction

In this paper, we study the effects of professionals’ survey-based inflation expectations on inflation for a large number of 36 economies, using dynamic cross-country panel estimation of New-Keynesian Phillips curves. We also study how the effect of inflation expectations on inflation depends on the inflation environment.

In standard theoretical economic models used for monetary policy analysis, inflation expectations matter for inflation (Clarida et al., 1999; Smets, 2003; Woodford, 2003). The role of inflation expectations has also played a central role in the recent debate on monetary policy frameworks, such as average inflation targeting, which attribute a key role to expected inflation. This has been the case for the monetary policy strategy reviews of both the Federal Reserve and the European Central Bank (ECB) (Eurosystem Work Stream on Inflation Expectations, 2021; Powell and Wessel, 2020). Partly based on the role of inflation expectations, the Federal Reserve adopted an average inflation targeting framework in 2020, and the ECB adopted a symmetric inflation target of 2% in 2021 (a change from an inflation aim of below but close to 2% previously).

Coibion et al. (2018) argued for a more systematic inclusion of real-time survey-based expectations in macroeconomic analyses. Coibion et al. (2018) found that it is important to include survey-based inflation expectations in the estimation of the New-Keynesian Phillips curve, and we do so in this paper. They noted that while New-Keynesian Phillips curves are derived under the assumption of full information rational expectations, and subjective inflation expectations deviate from this, Adam and Padula (2011) showed that survey-based expectations can be used if private agents follow the law of iterated expectations, which is only a weaker assumption. This constraint is satisfied, for example, when agents are rational but not sufficiently informed, and there is some evidence consistent with this condition being met (Coibion and Gorodnichenko, 2012, 2015a).

There is no consensus in the empirical literature on whether inflation expectations matter for inflation. This is the case since the effect of inflation expectations is difficult to distinguish from the effects of past inflation outturns and of trend inflation. On the one hand, some evidence for individual countries, such as the United States, suggests that there is no significant effect of inflation expectations on inflation (Rudd, 2021, and references therein). On the other hand, other evidence suggests that measures of inflation expectations matter for inflation, including in cross-country estimates of Phillips curves (Brissimis and Magginas, 2008; Coibion and Gorodnichenko, 2015b; Coibion et al. (2018); Forbes, 2019; Galí and Gertler, 1999; Kohlscheen and Moessner, 2022).

Resurgent consumer demand, supply chain bottlenecks and rising energy costs pushed up inflation globally in the wake of the pandemic. An important question is whether an increase in inflation leads to second-round effects on inflation, which could lead to more persistent increases in inflation. This could happen if inflation expectations increase, and if these higher inflation expectations lead to higher inflation.

We use a hybrid New-Keynesian Phillips curve framework with cross-country dynamic panel estimation based on Jasova et al. (2019, 2020), which includes a forward-looking inflation expectations term, in order to be consistent with the New-Keynesian Phillips curve framework which has commonly been used in macroeconomic and monetary policy analysis for capturing inflation dynamics. ¹ This approach allows us to exploit cross-country variation to avoid the difficulties of identification present for country-specific estimates, which were discussed by Reichlin (2018) and Forbes (2019) for the output gap. Here, we use this approach exploiting cross-country variation to also avoid some of the difficulties of identification of the effects of inflation expectations present with country-specific estimates discussed in the study by Rudd (2021).

We use survey-based inflation expectations of professionals, since they are available on a harmonised basis for the large number of countries in our sample, and since they are not distorted by risk and liquidity premia, in contrast to financial market-based measures. ²

We find that professionals’ survey-based inflation expectations have a significant effect on inflation. We also find evidence that these inflation expectations matter significantly more for inflation when inflation is higher. This suggests that second-round effects on inflation via rising inflation expectations are more relevant when inflation is already high.

The remainder of the paper is organised as follows. Section 2 introduces the data, section 3 presents the method and results and section 4 concludes.

2. Data

Data on seasonally adjusted headline consumer price indices (CPIs) come from Datastream and national sources. We use data on professionals’ survey-based CPI inflation expectations. These are taken from Consensus Economics surveys for next-year CPI inflation expectations.

Data on output gaps (as a percentage of potential Gross Domestic Product (GDP)) were obtained from the Organisation for Economic Co-operation and Development (OECD), and linearly interpolated from annual data. Nominal effective exchange rate indices (broad indices, quarterly averages) are taken from the Bank for International Settlements (BIS), where an increase indicates an appreciation of the domestic currency. Brent oil prices (quarterly averages, US dollar per barrel) are from Datastream.

We consider the following 36 economies: the OECD economies Austria, Australia, Belgium, Canada, Chile, the Czech Republic, Denmark, Estonia, euro area, Finland, France, Germany, Great Britain, Greece, Hungary, Ireland, Israel, Italy, Japan, Latvia, Lithuania, Mexico, the Netherlands, Norway, New Zealand, Poland, Portugal, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland and the United States, and the OECD candidate countries Bulgaria and Romania. The sample period is from 2000Q1 to 2021Q1 at quarterly frequency.

3. Method and results

To study the effects of inflation expectations on inflation, we estimate the following New-Keynesian Phillips curve based on the studies by Jasova et al. (2019, 2020), using a panel of 36 economies:

(1) $$ {\unicode{x03C0}}_{\mathrm{it}}=\unicode{x03B8}\;{\pi}_{it}^e+\unicode{x03C1}\;{\unicode{x03C0}}_{\mathrm{it}-1}+\unicode{x03D5}\;{outputgap}_{it}+\mu\;\Delta {NEER}_{it}+\unicode{x03BB}\;{\unicode{x03C0}}_{\mathrm{t}}^{\mathrm{oil}}+{\alpha}_i+{\varepsilon}_{it.} $$

where $ {\unicode{x03C0}}_{\mathrm{it}} $ denotes quarter-on-quarter (q/q) seasonally adjusted CPI inflation at annualised rates (saar) in percent, calculated from log differences in quarterly seasonally adjusted CPIs in country $ i $ at time t; $ {outputgap}_{it} $ denotes the output gap; $ {\pi}_{it}^e $ denotes next-year CPI inflation expectations from Consensus Economics surveys, year-on-year (y/y) in percent; $ \Delta {NEER}_{it} $ is the q/q change in the nominal effective exchange rate in percent, calculated from the log change in the nominal effective exchange rate, with an increase indicating an appreciation of the currency; $ {\unicode{x03C0}}_{\mathrm{t}}^{\mathrm{oil}} $ denotes q/q oil price inflation (annualised) calculated from log-differences in quarterly oil prices. Finally, $ {\alpha}_i $ are country fixed effects to control for observed and unobserved country heterogeneity. We use robust standard errors clustered at the country level. Equation (1) is our baseline specification.

For robustness, we also estimate the following specification, where we replace oil price changes by time fixed effects, $ {\beta}_{\mathrm{t}} $ , in order to control for all observed and unobserved variation in common global factors,

(2) $$ {\unicode{x03C0}}_{\mathrm{it}}=\unicode{x03B8}\;{\pi}_{it}^e+\rho\;{\unicode{x03C0}}_{\mathrm{it}-1}+\unicode{x03D5}\;{outputgap}_{it}+\mu\;\Delta {NEER}_{it}+{\alpha}_i+{\beta}_t+{\varepsilon}_{it.} $$

For robustness, we also use the system generalised method of moments (GMMs) following Arellano and Bover (1995) and Blundell and Bond (1998) for panel data with endogenous explanatory variables.

Our Phillips curve estimates for Equations (1) and (2) are shown in Table 1. We can see that in both specifications, the coefficient on the output gap is significantly positive, implying that in our cross-country, dynamic panel setting the New-Keynesian Phillips curve is “alive and well” (see also Coibion and Gorodnichenko, 2015b). We can also see that the coefficient on lagged inflation is significantly positive, so that our dynamic panel specification is appropriate. We find that inflation expectations have a significantly positive effect on inflation at the 1% significance level, with a coefficient which is similar for both specifications, at 1.17 and 1.15, respectively. Oil prices have a significantly positive effect on inflation at the 1% level. Moreover, exchange rate depreciations lead to significantly higher inflation, as would be expected, also at the 1% significance level in both specifications.

Table 1. CPI inflation versus inflation expectations

Note: Fixed effects panel estimation; sample period: 2000Q1-2021Q1. ***/**/*denote statistical significance at 1/5/10% confidence level. Robust standard errors clustered at the country level.

We next study whether the effects of inflation expectations on inflation depend on the inflation environment. We do so by adding an interaction term of inflation expectations with inflation lagged two periods, according to ³

(3) $$ {\unicode{x03C0}}_{\mathrm{it}}=\unicode{x03B8}\;{\pi}_{it}^e+\unicode{x03C1}\;{\unicode{x03C0}}_{\mathrm{it}-1}+\unicode{x03D5}\;{outputgap}_{it}+\mu\;\Delta {NEER}_{it}+\unicode{x03B3}\;{\pi}_{it}^e\;{\unicode{x03C0}}_{\mathrm{it}-2}+\unicode{x03BB}\;{\unicode{x03C0}}_{\mathrm{t}}^{\mathrm{oil}}+{\alpha}_i+{\varepsilon}_{it.} $$

The results for Equation (3) are shown in Table 2. For robustness, we also show the corresponding results in Table 2 when replacing oil price changes by time fixed effects in Equation (3). For both specifications, the effect of inflation expectations remains significantly positive at the 1% level. We find that the coefficient on the interaction term of inflation expectations with inflation lagged two periods is significantly positive at the 1% significance level, with a magnitude which is similar for both specifications, at 0.018 and 0.019, respectively. These results imply that inflation expectations have a stronger effect on inflation when inflation is higher. The coefficients on the remaining variables continue to be significant and have the expected signs.

Table 2. CPI inflation versus inflation expectations: with interaction term

Note: Fixed effects panel estimation; sample period: 2000Q1-2021Q1. ***/**/*denote statistical significance at 1/5/10% confidence level. Robust standard errors clustered at the country level.

For robustness, we also estimate Equations (1) to (3) using system GMM. The corresponding results are shown in Table 3, and we can see that the results are generally robust to using this method, both when including and when excluding the interaction term of inflation expectations with inflation lagged two periods.

Table 3. CPI inflation versus inflation expectations: system GMM estimation

Note: System GMM estimation using the studies by Arellano and Bover (1995) and Blundell and Bond (1998) dynamic panel estimator; sample period: 2000Q1-2021Q1. ***/**/*denote statistical significance at 1/5/10% confidence level. Robust standard errors.

^a Reports p-values for the null hypothesis that the instruments used are not correlated with the residuals.

^b Reports p-values for the null hypothesis that the errors in the first difference regression exhibit no second-order serial correlation.

We therefore find evidence that inflation expectations have a significantly larger effect on inflation when inflation is high. This suggests that second-round effects via the effects of higher inflation expectations on inflation are more of a problem in a high-inflation environment.

3.1. Quantile regressions

In order to study whether the effects of inflation expectations on inflation depend on the level of inflation, we next also estimate quantile regressions for the specification of Equation (1), at the 10th, 25th (lower quartile), 50th (median), 75th (upper quartile) and 90th percentiles. This allows us to determine whether the effects of inflation expectations on inflation differ when inflation is in the upper tail of the inflation distribution, compared with at the median level or in the lower tail of the distribution.

Quantile regressions weigh observations in the neighbourhood of the quantile of interest more heavily, and weigh other observations less heavily, corresponding to a reduction in sample size. We adopt the quantiles‐via‐moments estimation method of Machado and Santos Silva (2019), using their xtqreg command in Stata, as in the study by Kiley (2021).

The results of the quantile regressions for Equation (1) are shown in Table 4. We can see that for the median regression (50th percentile), the coefficient of inflation expectations is similar to that for the mean fixed effects panel regression shown in Table 1, at 1.15 compared with 1.17, and it is also significant at the 1% level. Figure 1 shows the results for the coefficients of inflation expectations for q/q inflation by percentile together with 90% confidence intervals. We can see that the coefficient of inflation expectations on q/q inflation is higher at larger percentiles, but not significantly so at the 10% level. For each of the percentiles, the coefficients on the other variables all remain significant and with the expected signs.

Table 4. CPI inflation (q/q) versus inflation expectations: quantile regressions

Note: Fixed effects panel estimation; sample period: 2000Q1-2021Q1. ***/**/*denote statistical significance at 1/5/10% confidence level.

Figure 1. Effects of inflation expectations on inflation (q/q) by percentile. Notes: Responses of CPI inflation to 1 percentage point increase in inflation expectations, in percent. Estimates using quantile regressions based on equation (1). Effect on CPI inflation (q/q saar), with 90% confidence intervals.

In order to study the effects of inflation expectations on inflation over a longer horizon, we also estimate these quantile regressions when using local projections based on the study by Jordà (2005),

(4) $$ {\unicode{x03C0}}_{\mathrm{it};\mathrm{h}}=\unicode{x03B8}\;{\pi}_{it}^e+\unicode{x03C1}\;{\unicode{x03C0}}_{\mathrm{it}-1}+\unicode{x03D5}\;{outputgap}_{it}+\mu\;\Delta {NEER}_{it}+\unicode{x03BB}\;{\unicode{x03C0}}_{\mathrm{t}}^{\mathrm{oil}}+{\alpha}_i+{\varepsilon}_{it.} $$

where $ {\unicode{x03C0}}_{\mathrm{it};\mathrm{h}} $ denotes the seasonally adjusted annualised CPI inflation rate at time t over h quarters in country $ i $ at time t, calculated from the log-difference between the CPI index at time t-1+h and the CPI index at time t-1. Here, h=1 corresponds to the q/q seasonally adjusted annualised inflation rate $ {\unicode{x03C0}}_{\mathrm{it}} $ used above, and h=4 corresponds to year-on-year (y/y) inflation.

The results of the quantile regressions at the yearly horizon (h = 4) for Equation (4) are shown in Table 5. Also shown in column I of Table 5 for comparison are the results of the mean fixed effects panel regression for Equation (4). We can see that for the median regression, the coefficient on inflation expectations is again similar to that for the mean fixed effects panel regression, at 0.75 compared with 0.76, and it is also significant at the 1% level.

Table 5. CPI inflation (y/y) versus inflation expectations

Note: Fixed effects panel estimation; sample period: 2000Q1-2021Q1. ***/**/*denote statistical significance at 1/5/10% confidence level.

Figure 2 shows the results for the coefficients of inflation expectations for y/y inflation by percentile together with 90% confidence intervals. We can see that the coefficients of inflation expectations are larger at higher percentiles of the inflation distribution. We find that at the yearly horizon, the coefficient of inflation expectations is significantly larger at the 10% level for the upper tail of the inflation distribution (the 90th percentile) than for the lower tail of the inflation distribution (the 10th percentile). Again, for each of the percentiles, the coefficients on the other variables all remain significant and with the expected signs. These results therefore also suggest that second-round effects via the effects of higher inflation expectations on inflation are more of a problem in a high-inflation environment.

Figure 2. Effects of inflation expectations on inflation (y/y) by percentile. Notes: Responses of CPI inflation to 1 percentage point increase in inflation expectations, in percent. Estimates using quantile regressions based on equation (4). Effect on CPI inflation (y/y), with 90% confidence intervals.

4. Conclusions

We studied the effects of professionals’ survey-based inflation expectations on inflation for 36 economies, using dynamic cross-country panel estimation of New-Keynesian Phillips curves. We also studied how the effects of inflation expectations on inflation depend on the inflation environment.

We find that inflation expectations have a significant effect on inflation. We also find evidence that inflation expectations matter significantly more for inflation when inflation is higher. This suggests that second-round effects via the effects of higher inflation expectations on inflation are more relevant in a high-inflation environment.