VAR Models , Forecasting , Model Selection , Shrinkage
Abstract:
This paper provides an empirical comparison of various selection and penalized regression
approaches for forecasting with vector autoregressive systems. In particular, we investigate the effect of the system size as well as the effect of various prior specification choices on the relative
and overall forecasting performance of the methods. The data set is a typical macroeconomic
quarterly data set for the US. We find that these specification choices are crucial for most
methods. Conditional on certain choices, the variation across different approaches is relatively
small. There are only a few methods which are not competitive under any scenario. For single
series, we find that increasing the system size can be helpful - depending on the employed
shrinkage method.
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