Journal
INTERNATIONAL JOURNAL OF FORECASTING
Volume 39, Issue 4, Pages 1820-1838Publisher
ELSEVIER
DOI: 10.1016/j.ijforecast.2022.09.002
Keywords
Forecasting; Model averaging; Prediction pooling; DSGE models; Macroeconomic variables
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This study provides a comprehensive assessment of the predictive power of combinations of dynamic stochastic general equilibrium (DSGE) models. The results show that using mixtures of DSGE models produces competitive forecasts compared to individual specifications.
We provide a comprehensive assessment of the predictive power of combinations of dynamic stochastic general equilibrium (DSGE) models for GDP growth, inflation, and the interest rate in the euro area. We employ a battery of static and dynamic pooling weights based on Bayesian model averaging principles, prediction pools, and dynamic factor representations, and entertain six different DSGE specifications and five prediction weighting schemes. Our results indicate that exploiting mixtures of DSGE models produces competitive forecasts compared to individual specifications for both point and density forecasts over the last three decades. Although these combinations do not tend to systematically achieve superior forecast performance, we find improvements for particular periods of time and variables when using prediction pooling, dynamic model averaging, and combinations of forecasts based on Bayesian predictive synthesis.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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