developed measures of partisan conflict, geopolitical risks and news-based VIX (NVIX). 5 These authors indicate that although in a general sense uncertainty is defined as the conditional volatility of an unforecastable disturbance, the empirical literature has usually relied on, which in turn can pick up fluctuations that are actually predictable and hence, can erroneously be attributable to uncertainty. Thus, it is important to distinguish between uncertainty in a series and its conditional volatility, i.e., properly measuring uncertainty would require one to remove the forecastable component of the considered series before computing the conditional volatility. In this sense, uncertainty in a series is not necessarily equivalent to the conditional volatility of the raw

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We contribute to recent research on the joint evaluation of the properties of macroeconomic forecasts in a multivariate setting. The specific property of forecasts that we are interested in is their joint efficiency. We study the joint efficiency of forecasts by means of multivariate random forests, which we use to model the links between forecast errors and predictor variables in a forecaster's information set. We then use permutation tests to study whether the Mahalanobis distance between the predicted forecast errors for the growth and inflation forecasts of four leading German economic research institutes and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. We reject joint efficiency in several cases, but also document heterogeneity across research institutes with regard to the joint efficiency of their forecasts.