Forecasting Macroeconomic Variables Under Model Instability

44 Pages Posted: 27 Jun 2016

See all articles by Davide Pettenuzzo

Davide Pettenuzzo

Brandeis University - International Business School

Allan Timmermann

UCSD ; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 2 versions of this paper

Date Written: June 2016

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: GDP growth, inflation, regime switching, stochastic volatility, time-varying parameters

JEL Classification: C22, C53

Suggested Citation

Pettenuzzo, Davide and Timmermann, Allan, Forecasting Macroeconomic Variables Under Model Instability (June 2016). CEPR Discussion Paper No. DP11355. Available at SSRN: https://ssrn.com/abstract=2801036

Davide Pettenuzzo (Contact Author)

Brandeis University - International Business School ( email )

Mailstop 32
Waltham, MA 02454-9110
United States

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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