An approach to increasing forecast-combination accuracy through VAR error modeling

No 6818, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

Abstract: We consider a situation in which the forecaster has available M individual forecasts of a univariate target variable. We propose a 3-step procedure designed to exploit the interrelationships among the M forecast-error series (estimated from a large time-varying parameter VAR model of the errors, using past observations) with the aim of obtaining more accurate predictions of future forecast errors. The refined future forecast-error predictions are then used to obtain M new individual forecasts that are adapted to the information from the estimated VAR. The adapted M individual forecasts are ultimately combined and any potential accuracy gains of the adapted combination forecasts analyzed. We evaluate our approach in an out-of-sample forecasting analysis, using a well-established 7-country data set on output growth. Our 3-step procedure yields substantial accuracy gains (in terms of loss reductions ranging between 6.2% up to 18%) for the simple average and three time-varying-parameter combination forecasts.

Keywords: Forecast combinations; large time-varying parameter VARs; Bayesian VAR estimation; state-space model; forgetting factors; dynamic model averaging. (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2018-02
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Related works:
Journal Article: An approach to increasing forecast‐combination accuracy through VAR error modeling (2021)
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