Artikel
A nonparametric evaluation of the optimality of German export and import growth forecasts under flexible loss
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Economies ; ISSN: 2227-7099 ; Volume: 7 ; Year: 2019 ; Issue: 3 ; Pages: 1-23 ; Basel: MDPI
- Classification
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Wirtschaft
Forecasting Models; Simulation Methods
Trade: Forecasting and Simulation
Macroeconomic Aspects of International Trade and Finance: Forecasting and Simulation: Models and Applications
- Subject
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flexible loss
forecast optimality
German economic research institutes
random forests
trade forecasts
- Event
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Geistige Schöpfung
- (who)
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Behrens, Christoph
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2019
- DOI
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doi:10.3390/economies7030093
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Behrens, Christoph
- MDPI
Time of origin
- 2019
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