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.