Working Paper: CEPR ID: DP2961
Authors: Fabio Canova; Matteo Ciccarelli
Abstract: We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model that accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for hierarchical and for Minnesota-type priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.
Keywords: Bayesian methods; Panel VAR; Markov chains; Monte Carlo methods; Forecasting; Turning points
JEL Codes: C11; C15; E32; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Bayesian panel VAR model (C32) | forecasting accuracy (C53) |
| Bayesian panel VAR model (C32) | predictions of output growth rates (O40) |
| Bayesian panel VAR model (C32) | turning point predictions (C53) |