Factor Model Forecasting: A Bayesian Model Averaging (BMA) Perspective

27 Pages Posted: 23 Jan 2014 Last revised: 9 Mar 2014

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: 2014

Abstract

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

Suggested Citation

Korobilis, Dimitris, Factor Model Forecasting: A Bayesian Model Averaging (BMA) Perspective (2014). Available at SSRN: https://ssrn.com/abstract=2381896 or http://dx.doi.org/10.2139/ssrn.2381896

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

Register to save articles to
your library

Register

Paper statistics

Downloads
76
Abstract Views
479
rank
321,757
PlumX Metrics