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Journal of Forecasting
Article . 2016 . Peer-reviewed
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Article . 2017
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Article . 2017
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Research . 2014
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Forecast Combinations in a DSGE‐VAR Lab

Forecast combinations in a DSGE-VAR lab
Authors: COSTANTINI M; GUNTER U; KUNST R;

Forecast Combinations in a DSGE‐VAR Lab

Abstract

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.

Countries
Austria, Austria, Italy, United Kingdom
Keywords

model selection, Time series, 101018 Statistik, 330, Dynamic stochastic general equilibrium theory, 502025 Ökonometrie, forecasting, Model selection, SHOCKS, Inference from stochastic processes and prediction, BUSINESS CYCLES, Applications of statistics to economics, Bates-Granger weighting, Encompassing tests, simple averaging, US, 101018 Statistics, ddc:330, 502018 Macroeconomics, VWL, 502018 Makroökonomie, combining forecasts, Combining forecasts, Economic time series analysis, TESTS, FRICTIONS, 502025 Econometrics, forecasting; combining forecasts; encompassing tests; model selection; time series, DSGE-VAR model, Combining forecasts, encompassing tests, model selection, time series, DSGE-VAR model, time series, GENERAL EQUILIBRIUM-MODELS, encompassing tests, SRA, Forecasting

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
58
Top 1%
Top 10%
Average
bronze