A1131
Title: Subspace shrinkage in conjugate Bayesian vector autoregressions
Authors: Florian Huber - University of Salzburg (Austria) [presenting]
Gary Koop - University of Strathclyde (United Kingdom)
Abstract: Macroeconomists using large datasets often face the choice of working with either a large Vector Autoregression (VAR) or a factor model. We develop methods for combining the two using a subspace shrinkage prior. Subspace priors shrink towards a class of functions rather than directly forcing the parameters of a model towards some pre-specified location. We develop a conjugate VAR prior which shrinks towards the subspace which is defined by a factor model. The approach allows for estimating the strength of the shrinkage as well as the number of factors. After establishing the theoretical properties of our proposed prior, we carry out simulations and apply it to US macroeconomic data. Using simulations we show that our framework successfully detects the number of factors. In a forecasting exercise involving a large macroeconomic data set we find that combining VARs with factor models using our prior can lead to forecast improvements.
