Forecasting with Factor Models: A Bayesian Model Averaging Perspective
MPRA Paper from University Library of Munich, Germany
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 (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 C53 C63 E17 G01 G17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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