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Bayesian Model Averaging under Regime Switching with Application to Cyclical Macro Variable Forecasting
Author(s) -
Shi Jianmin
Publication year - 2016
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2375
Subject(s) - econometrics , bayesian probability , economics , variable (mathematics) , inflation (cosmology) , macro , regression , bayesian inference , bayesian vector autoregression , computer science , statistics , mathematics , artificial intelligence , mathematical analysis , physics , theoretical physics , programming language
Abstract Model uncertainty and recurrent or cyclical structural changes in macroeconomic time series dynamics are substantial challenges to macroeconomic forecasting. This paper discusses a macro variable forecasting methodology that combines model uncertainty and regime switching simultaneously. The proposed predictive regression specification permits both regime switching of the regression parameters and uncertainty about the inclusion of forecasting variables by employing Bayesian model averaging. In an empirical exercise involving quarterly US inflation, we observed that our Bayesian model averaging with regime switching leads to substantial improvements in forecast performance, particularly in the medium horizon (two to four quarters). Copyright © 2015 John Wiley & Sons, Ltd.