DP11355 Forecasting Macroeconomic Variables under Model Instability

Author(s): Davide Pettenuzzo, Allan G Timmermann
Publication Date: June 2016
Keyword(s): GDP growth, inflation, regime switching, stochastic volatility, time-varying parameters
JEL(s): C22, C53
Programme Areas: Financial Economics
Link to this Page: www.cepr.org/active/publications/discussion_papers/dp.php?dpno=11355

We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. GDP growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model which is a specification that allows for time-varying parameters and stochastic volatility.