Forecasting Macroeconomic Variables Under Model Instability

41 Pages Posted: 9 May 2015

See all articles by Davide Pettenuzzo

Davide Pettenuzzo

Brandeis University - Department of Economics

Allan Timmermann

UCSD ; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 2 versions of this paper

Date Written: May 6, 2015

Abstract

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.

Keywords: Time-varying parameters, regime switching, change point models, stochastic volatility, GDP growth forecasts, inflation forecasts

JEL Classification: C22, C53

Suggested Citation

Pettenuzzo, Davide and Timmermann, Allan, Forecasting Macroeconomic Variables Under Model Instability (May 6, 2015). Available at SSRN: https://ssrn.com/abstract=2603879 or http://dx.doi.org/10.2139/ssrn.2603879

Davide Pettenuzzo (Contact Author)

Brandeis University - Department of Economics ( email )

Waltham, MA 02454-9110
United States
781.736.2834 (Phone)
781.736.2269 (Fax)

Allan Timmermann

UCSD ( email )

9500 Gilman Drive
La Jolla, CA 92093-0553
United States
858-534-0894 (Phone)

HOME PAGE: http://rady.ucsd.edu/people/faculty/timmermann/

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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