Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257025 
Year of Publication: 
2019
Citation: 
[Journal:] Economies [ISSN:] 2227-7099 [Volume:] 7 [Issue:] 3 [Article No.:] 93 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-23
Publisher: 
MDPI, Basel
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.
Subjects: 
flexible loss
forecast optimality
German economic research institutes
random forests
trade forecasts
JEL: 
C53
F17
F47
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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