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Forecast Combinations in a DSGE‐VAR Lab
Author(s) -
Costantini Mauro,
Gunter Ulrich,
M. Kunst Robert
Publication year - 2017
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2427
Subject(s) - weighting , autoregressive model , dynamic stochastic general equilibrium , bates , econometrics , series (stratigraphy) , us dollar , vector autoregression , simple (philosophy) , mathematics , computer science , economics , monetary policy , engineering , exchange rate , finance , medicine , paleontology , philosophy , epistemology , biology , monetary economics , radiology , aerospace engineering
We explore the benefits of forecast combinations based on forecast‐encompassing tests compared to simple averages and to Bates–Granger combinations. We also consider a new combination algorithm that fuses test‐based and Bates–Granger weighting. For a realistic simulation design, we generate multivariate time series samples from a macroeconomic DSGE‐VAR (dynamic stochastic general equilibrium–vector autoregressive) model. Results generally support Bates–Granger over uniform weighting, whereas benefits of test‐based weights depend on the sample size and on the prediction horizon. In a corresponding application to real‐world data, simple averaging performs best. Uniform averages may be the weighting scheme that is most robust to empirically observed irregularities. Copyright © 2016 John Wiley & Sons, Ltd.