I analyze the joint efficiency of export and import forecasts by leading economic research institutes for the years 1970 to 2017 for Germany in a multivariate setting. To this end, I compute, in a first step, multivariate random forests in order to model links between forecast errors and a forecaster's information set, consisting of several trade and other macroeconomic predictor variables. I use the Mahalanobis distance as performance criterion and, in a second step, permutation tests to check

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... whether the Mahalanobis distance between the predicted forecast errors for the trade forecasts and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. I find evidence for joint forecast inefficiency for two forecasters, however, for one forecaster I cannot reject joint forecast efficiency. For the other forecasters, joint forecast efficiency depends on the examined forecast horizon. I find evidence that real macroeconomic variables as opposed to trade variables are inefficiently included in the analyzed trade forecasts. Finally, I compile a joint efficiency ranking of the forecasters.