Abstract
In this paper, we evaluate a model that describes real-time inflation data together with the inflation expectations measured by the Survey of Professional Forecasters (SPF). We work with a second-order autoregressive model in which the agents learn over time the intercept and persistence coefficients based on real-time data. To model the process of revisions in real time data, we allow for news and noise disturbances. In contrast to the usual time-varying parameter vector autoregression, we use non-linear Kalman filter techniques to estimate the time-varying coefficients of the underlying inflation process. We identify systematic changes in the persistence of the inflation process and in the long-run expected inflation rate that are implied by the model. The inflation forecasts implied by the model are then compared with the SPF forecasts. As we cannot reject the hypothesis that the SPF forecasts are produced based on our model, we re-estimate the model using Survey nowcasts and forecasts as additional observables. This augmented model does not change the nature and magnitude of the time variation in the coefficients of the autoregressive model, but it does help to reduce the uncertainty in the estimates. Overall, the estimated time-variation confirms our results on the perceived inflation process present in estimated DSGE models with learning (Slobodyan and Wouters, 2012a, 2012b).
Highlights
We analyse which types of forecasting models and learning dynamics are consistent with the inflation expectations measured by the Survey of Professional Forecasters (SPF)
We compare the time variability of parameters in univariate statistical models of inflation with the time-variation produced by the adaptive learning updating in medium-scale dynamic stochastic general equilibrium (DSGE) models
Survey of Professional Forecasters data on inflation expectations are consistent with these timevarying parameters (TVP) models; treating these survey data as observables for the inflation forecasts helps to pin down more precisely the time-varying path of the parameters
Summary
We analyse which types of forecasting models and learning dynamics are consistent with the inflation expectations measured by the Survey of Professional Forecasters (SPF). After evaluating the forecasting model based on inflation releases only, we re-estimate the model taking the SPF nowcasts and forecasts as additional observations to exploit the information from the SPF more intensively. Milani (2011), using SPF data combined with real-time data, observes large deviations between model forecasts and SPF evidence, and he suggests that these expectation ‘shocks’ might be an important source of business cycle fluctuations In contrast with these findings, our results suggest that simple time-varying models can produce forecasts that are very similar to the SPF data, and our augmented june 2021 models, including the SPF forecasts, require minimal measurement errors to reproduce these survey forecasts. We consider this exercise a first exploration of the SPF data and leave the full integration of survey data into the DSGE model for future research
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