A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss

Christoph Behrens
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Christoph Behrens: Department of Economics, Helmut Schmidt University, Holstenhofweg 85, 22043 Hamburg, Germany

Economies, 2019, vol. 7, issue 3, 1-23

Abstract: This study contributes to research on the nonparametric evaluation of German trade forecasts. To this end, I compute random classification and regression forests to analyze the optimality of annual German export and import growth forecasts from 1970 to 2017. A forecast is considered as optimal if a set of predictors, which models the information set of a forecaster at the time of forecast formation, has no explanatory power for the corresponding (sign of the) forecast error. I analyze trade forecasts of four major German economic research institutes, a collaboration of German economic research institutes, and one international forecaster. For trade forecasts with a horizon of half-a-year, I cannot reject forecast optimality for all but one forecaster. In the case of a forecast horizon of one year, forecast optimality is rejected in more cases if the underlying loss function is assumed to be quadratic. Allowing for a flexible loss function results in more favorable assessment of forecast optimality.

Keywords: trade forecasts; German economic research institutes; forecast optimality; flexible loss; random forests (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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