Evaluating the joint efficiency of German trade forecasts - a nonparametric multivariate approach

Abstract

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 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.

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

“…Compared with machine learning algorithms such as backpropagation (BP) neural networks, support vector machines (SVMs), and decision trees, the RF algorithm has higher prediction accuracy (Yu et al, 2019;Zhang et al, 2020d). At present, it is mainly used in the fields of medicine (Pan et al, 2017), economics (Behrens, 2020), and management (Grushka-Cockayne et al, 2017;Mueller, 2020). In the field of engineering, the RF algorithm has been studied for crack prediction (Bhattacharya & Mishra, 2018), energy evaluation, and construction management (Pan & Zhang, 2020.…”