Data Revisions in the Estimation of DSGE Models

By Miguel Casares and Jesús Vázquez Pérez

Revisions of US macroeconomic data are not white-noise. They are persistent, correlated with real-time data, and with high variability (around 80% of volatility observed in US real-time data). Their business cycle effects are examined in an estimated DSGE model extended with both real-time and final data. After implementing a Bayesian estimation approach, the role of both habit formation and price indexation fall significantly in the extended model. The results show how revision shocks of both output and inflation are expansionary because they occur when real-time published data are too low and the Fed reacts by cutting interest rates. Consumption revisions, by contrast, are countercyclical as consumption habits mirror the observed reduction in real-time consumption. In turn, revisions of the three variables explain 9.3% of changes of output in its long-run variance decomposition.

In a typical DSGE model, all agents take decisions given the current state of the economy. The estimate of that state is subject to revision, whose amplitude is in the same order of magnitude as the business cycle. So these revisions got to have an impact and they do as this paper shows.

Note that it makes use of the real-time dataset at the Federal Reserve Bank of Philadelphia. The Federal Reserve Bank of St. Louis also has one, ALFRED, which is derived from the popular FRED.

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