This paper argues that inferring long-horizon asset-return predictability from the properties of vector autoregressive (VAR) models on relatively short spans of data is potentially unreliable. We illustrate the problems that can arise by re-examining the findings of Bekaert and Hodrick (1992), who detected evidence of in-sample predictability in international equity and foreign exchange markets using VAR methodology for a variety of countries over the period 1981-1989. The VAR predictions are significantly biased in most out-of-sample forecasts and are conclusively outperformed by a simple benchmark model at horizons of up to six months. This remains true even after corrections for small sample bias and the introduction of Bayesian parameter restrictions. A Monte Carlo analysis indicates that the data are unlikely to have been generated by a stable VAR. This conclusion is supported by an examination of structural break statistics. Implied long-horizon statistics calculated from the VAR parameter estimates are shown to be very unreliable.