Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts

Abstract

We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short-term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts obtained from a VAR fitted by our new shrinkage approach typically yield smaller mean squared forecast errors than the forecasts obtained from a range of benchmark methods.