Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs
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We investigate the ability of small- and medium-scale Bayesian VARs (BVARs) to produce accurate macroeconomic (output and inflation) and credit (loans and lending rate) out-of-sample forecasts during the latest Greek crisis. We implement recently proposed Bayesian shrinkage techniques based on Bayesian hierarchical modeling, and we evaluate the information content of forty-two (42) monthly macroeconomic and financial variables in terms of point and density forecasting. Alternative competing models employed in the study include Bayesian autoregressions (BARs) and time-varying parameter VARs with stochastic volatility, among others. The empirical results reveal that, overall, medium-scale BVARs enriched with economy-wide variables can considerably and consistently improve short-term inflation forecasts. The information content of financial variables, on the other hand, proves to be beneficial for the lending rate density forecasts across forecasting horizons. Both of the above-mentioned results are robust to alternative specification choices, while for the rest of the variables smaller-scale BVARs, or even univariate BARs, produce superior forecasts. Finally, we find that the popular, data-driven, shrinkage methods produce, on average, inferior forecasts compared to the theoretically grounded method considered here.
KeywordsForecasting Bayesian VARs Crisis Financial variables
JEL ClassificationC32 C53
I gratefully acknowledge three anonymous reviewers, Robert Kunst (the Editor), Heather Gibson and Hiona Balfoussia for their constructive and insightful comments and suggestions that considerably improved this article. I would also like to acknowledge Dimitris Malliaropulos and the colleagues from the Department of Economic Analysis and Research of the Bank of Greece for their helpful comments and discussions. The views expressed in this article do not necessarily represent Bank of Greece.
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