Prediction using several macroeconomic models

Gianni Amisano and John Geweke

No 1537, Working Paper Series from European Central Bank

Abstract: Prediction of macroeconomic aggregates is one of the primary functions of macroeconometric models, including dynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressions. This study establishes methods that improve the predictions of these models, using a representative model from each class and a canonical 7-variable postwar US data set. It focuses on prediction over the period 1966 through 2011. It measures the quality of prediction by the probability densities assigned to the actual values of these variables, one quarter ahead, by the predictive distributions of the models in real time. Two steps lead to substantial improvement. The first is to use full Bayesian predictive distributions rather than substitute a "plug-in" posterior mode for parameters. Across models and quarters, this leads to a mean improvement in probability of 50.4%. The second is to use an equally-weighted pool of predictive densities from the three models, which leads to a mean improvement in probability of 41.9% over the full Bayesian predictive distributions of the individual models. This improvement is much better than that a JEL Classification: C11, C51, C53

Keywords: analysis of variance; Bayesian model averaging; dynamic factor model; dynamic stochastic general equilibrium model; prediction pools; probability integral transform test; vector autoregression model (search for similar items in EconPapers)
Date: 2013-04
New Economics Papers: this item is included in nep-ecm and nep-for
Note: 337895
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Journal Article: Prediction Using Several Macroeconomic Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20131537

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