Factor Model Forecasting: A Bayesian Model Averaging (BMA) Perspective
27 Pages Posted: 23 Jan 2014 Last revised: 9 Mar 2014
Date Written: 2014
We use Bayesian factor regression models to construct a financial conditions index (FCI) for the U.S. Within this context we develop Bayesian model averaging methods that allow the data to select which variables should be included in the FCI or not. We also examine the importance of different sources of instability in the factors, such as stochastic volatility and structural breaks. Our results indicate that ignoring structural breaks in the loadings can be quite costly in terms of the forecasting performance of the FCI. Additionally, Bayesian model averaging can improve in specific cases the performance of the FCI, by means of discarding irrelevant financial variables during the estimation of the factor.
Keywords: financial stress, stochastic search variable selection, early-warning system, forecasting
JEL Classification: C11, C32, C52, C53, C66
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