VAR Model Averaging for Multi-Step Forecasting

Johannes Mayr () and Dirk Ulbricht

No Ifo Working Paper No. 48, Ifo Working Paper Series from Ifo Institute for Economic Research at the University of Munich

Abstract: Given the relatively low computational effort involved, vector autoregressive (VAR)models are frequently used for macroeconomic forecasting purposes. However, the usuallylimited number of observations obliges the researcher to focus on a relatively smallset of key variables, possibly discarding valuable information. This paper proposes aneasy way out of this dilemma: Do not make a choice. A wide range of theoretical andempirical literature has already demonstrated the superiority of combined to single-modelbased forecasts. Thus, the estimation and combination of parsimonious VARs, employingevery reasonably estimable combination of the relevant variables, pose a viable path ofdealing with the degrees of freedom restriction. The results of a broad empirical analysisbased on pseudo out-of-sample forecasts indicate that attributing equal weights systematicallyout-performs single models as well as most more refined weighting schemes interms of forecast accuracy and especially in terms of forecast stability.

Keywords: VAR-forecasting; model averaging (search for similar items in EconPapers)
JEL-codes: A10 C52 C53 E37 (search for similar items in EconPapers)
Date: 2007

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