VAR Model Averaging for Multi-Step Forecasting
Johannes Mayr () and
No 48, ifo Working Paper Series from ifo Institute - Leibniz Institute for Economic Research at the University of Munich
Given the relatively low computational effort involved, vector autoregressive (VAR) models are frequently used for macroeconomic forecasting purposes. However, the usually limited number of observations obliges the researcher to focus on a relatively small set of key variables, possibly discarding valuable information. This paper proposes an easy way out of this dilemma: Do not make a choice. A wide range of theoretical and empirical literature has already demonstrated the superiority of combined to single-model based forecasts. Thus, the estimation and combination of parsimonious VARs, employing every reasonably estimable combination of the relevant variables, pose a viable path of dealing with the degrees of freedom restriction. The results of a broad empirical analysis based on pseudo out-of-sample forecasts indicate that attributing equal weights systematically out-performs single models as well as most more refined weighting schemes in terms of forecast accuracy and especially in terms of forecast stability.
JEL-codes: A10 C52 C53 E37 (search for similar items in EconPapers)
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