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

Christoph Behrens

doi:10.3390/economies7030093

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.

Highlights

As one of the world’s main exporters, Germany’s trade policy has received much attention in recent years
Regarding the hyper-parameters of the random forests, I follow the common approach in the literature in setting the maximum number of terminal nodes of a given tree to five and setting the number of predictors used for growing a random forest to the square root of the total number of predictors
Assuming a quadratic loss function for the forecasters results in the rejection of forecast optimality for both longer-term export and import growth forecasts, whereas, under flexible loss, forecast optimality cannot be rejected for these forecasts

Summary

Introduction

As one of the world’s main exporters, Germany’s trade policy has received much attention in recent years. The research topics in this field are manifold, including studies on forecast revisions (Kirchgässner and Müller 2006), forecast accuracy (Heilemann and Stekler 2013), external assumptions of forecasts (Engelke et al 2019), forecaster rankings (Kirchgässner 1993; Sinclair et al 2016), or the economic value of forecasts (Döpke et al 2018) Most of these studies focus on the analysis of GDP and inflation forecasts by means of panel-based models (Döpke and Fritsche 2006; Müller et al 2019). I extend this branch of research by analyzing the optimality of German export growth and import growth forecasts under both quadratic and flexible loss. To this end, I build on research by Elliott et al (2005, 2008), who study optimal forecasts under asymmetric loss.

The Model

The Data

Empirical Results

Conclusions