Evaluating the joint efficiency of German trade forecasts - a nonparametric multivariate approach
Evaluating the joint efficiency of German trade forecasts - a nonparametric multivariate approach
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DOI：
10.1080/00036846.2020.1721423
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发表时间：
2020-02
影响因子：
2.2
通讯作者：
C. Behrens
中科院分区：
文献类型：
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作者：
C. Behrens
摘要我分析了主要经济研究机构对德国1970年至2017年出口和进口预测的联合效率。为此，我在第一步中计算了多变量随机森林，以便对预测误差和预报员的信息集之间的联系进行建模，这些信息集包括几个贸易和其他宏观经济预测变量。我使用马氏距离作为绩效标准，在第二步，通过置换检验检验贸易预测的预测误差与实际预测误差之间的马氏距离是否显著小于预测效率零假设下的马氏距离。我发现了两个预测者联合预测效率低下的证据，然而，对于一个预测者，我不能拒绝联合预测效率。对于其他预测者，联合预测的效率取决于所检查的预测期。我发现有证据表明，实际的宏观经济变量，而不是贸易变量，在分析的贸易预测中被低效地包括在内。最后，我编制了预测者的联合效率排名。
ABSTRACT I analyse 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 analysed trade forecasts. Finally, I compile a joint efficiency ranking of the forecasters.