Bayesian forecast combination in VAR-DSGE models

Abstract

We evaluate the performance of individual and combination forecasts produced by Bayesian vector autoregressions (Bayesian VARs) with economic and/or non-economic information. In particular, we conduct an out-of-sample forecasting experiment using Bayesian VARs with statistical and/or DSGE prior(s) over two subsamples, representing the so-called “Great Inflation” and “Great Moderation” periods, respectively. Our main findings are summarized as follows. First, in most cases, the inclusion of prior information, either statistical or economic prior(s), improves the accuracy of point forecasts produced by the Bayesian VARs, relative to the ARMA(1,1) benchmark. Second, forecast combination helps to produce unbiased forecasts in most cases. Moreover, it significantly improves the forecast accuracy of the macro-econometric models, especially during the “Great Moderation” period. Third, the selection of the weighting scheme in forecast combination, simple averaging or Bayesian model averaging (BMA), does not affect the conclusions made above. Finally, partially in line with the recent literature, we find a positive relationship between the persistence of inflation and its relative forecast accuracy at the four-quarter horizon.