ČAPEK, Jan, Jesús CRESPO CUARESMA, Niko HAUZENBERGER and Vlastimil REICHEL. Macroeconomic forecasting in the euro area using predictive combinations of DSGE models. INTERNATIONAL JOURNAL OF FORECASTING. NETHERLANDS: ELSEVIER, 2023, vol. 39, No 4, p. 1820-1838. ISSN 0169-2070. Available from: https://dx.doi.org/10.1016/j.ijforecast.2022.09.002.
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Basic information
Original name Macroeconomic forecasting in the euro area using predictive combinations of DSGE models
Authors ČAPEK, Jan (203 Czech Republic, guarantor, belonging to the institution), Jesús CRESPO CUARESMA (724 Spain, belonging to the institution), Niko HAUZENBERGER (40 Austria, belonging to the institution) and Vlastimil REICHEL (203 Czech Republic, belonging to the institution).
Edition INTERNATIONAL JOURNAL OF FORECASTING, NETHERLANDS, ELSEVIER, 2023, 0169-2070.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 50202 Applied Economics, Econometrics
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 7.900 in 2022
RIV identification code RIV/00216224:14560/23:00134014
Organization unit Faculty of Economics and Administration
Doi http://dx.doi.org/10.1016/j.ijforecast.2022.09.002
UT WoS 001075115800001
Keywords in English Forecasting; Model averaging; Prediction pooling; DSGE models; Macroeconomic variables
Tags International impact, Reviewed
Changed by Changed by: doc. Ing. Jan Čapek, Ph.D., učo 40604. Changed: 3/1/2024 12:21.
Abstract
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.
Links
GA17-14263S, research and development projectName: Dynamické průměrování předpovědí makroekonomických modelů
Investor: Czech Science Foundation
GA21-10562S, research and development projectName: O časově proměnné prediktivní schopnosti teoretických a empirických makroekonomických modelů
Investor: Czech Science Foundation
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  • a concrete person doc. Ing. Jan Čapek, Ph.D., učo 40604
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