C53 Forecasting and Other Model Applications
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This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.
This paper investigates the accuracy of forecasts from four DSGE models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed’s Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation the model forecasts are dominated by the Greenbook projections. A comparison with forecasts from Bayesian VARs shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts.
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.
This study uses Markov-switching models to evaluate the informational content of the term structure as a predictor of recessions in eight OECD countries. The empirical results suggest that for all countries the term spread is sensibly modelled as a two-state regime-switching process. Moreover, our simple univariate model turns out to be a filter that transforms accurately term spread changes into turning point predictions. The term structure is confirmed to be a reliable recession indicator. However, the results of probit estimations show that the markov-switching filter does not significantly improve the forecasting ability of the spread.
Modeling short-term interest rates as following regime-switching processes has become increasingly popular. Theoretically, regime-switching models are able to capture rational expectations of infrequently occurring discrete events. Technically, they allow for potential time-varying stationarity. After discussing both aspects with reference to the recent literature, this paper provides estimations of various univariate regime-switching specifications for the German three-month money market rate and bivariate specifications additionally including the term spread. However, the main contribution is a multi-step out-of-sample forecasting competition. It turns out that forecasts are improved substantially when allowing for state-dependence. Particularly, the informational content of the term spread for future short rate changes can be exploited optimally within a multivariate regime-switching framework.