Arbeitspapier
Dissecting models' forecasting performance
In this paper we suggest an approach to comparison of models' forecasting performance in unstable environments. Our approach is based on combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) suggested earlier in Welch and Goyal (2008) and the Bayesian change point analysis based on Barry and Hartigan (1993). The latter methodology provides the formal statistical analysis of the CSSFED time series which turned out to be a powerful graphical tool for tracking how the relative forecasting performance of competing models evolves over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.
- Sprache
-
Englisch
- Erschienen in
-
Series: KOF Working Papers ; No. 397
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Forecasting Models; Simulation Methods
- Thema
-
Forecasting
Forecast Evaluation
Change Point Detection
Bayesian Estimation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Siliverstovs, Boriss
- Ereignis
-
Veröffentlichung
- (wer)
-
ETH Zurich, KOF Swiss Economic Institute
- (wo)
-
Zurich
- (wann)
-
2015
- DOI
-
doi:10.3929/ethz-a-010692101
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Siliverstovs, Boriss
- ETH Zurich, KOF Swiss Economic Institute
Entstanden
- 2015
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