Methoden vergleichen

	Giacomini-White-Test auf bedingte prädiktive Fähigkeit	Zeitreihen-Kreuzvalidierung (rollierendes/expandierendes Fenster)
Fachgebiet	Ökonometrie	Ökonometrie
Familie	Hypothesis test	Process / pipeline
Entstehungsjahr	2006	2012
Urheber	Raffaella Giacomini & Halbert White	Christoph Bergmeir & José Benítez
Typ	Non-nested forecast comparison test	Forecast evaluation procedure
Wegweisende Quelle	Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.	Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213.
Aliasnamen	GW Test, Conditional Predictive Ability Test, Giacomini-White CPA Test, Koşullu Tahmin Yeteneği Testi	Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama
Verwandt	3	3
Zusammenfassung	The Giacomini-White (GW) test, introduced by Raffaella Giacomini and Halbert White in 2006, evaluates whether two competing forecasting methods have equal conditional predictive ability given information available at the time of forecast. Unlike unconditional tests such as the Diebold-Mariano test, it asks whether one method systematically outperforms the other in specific economic or market conditions, making it especially useful for practitioners who need state-dependent forecast comparisons.	Time-series cross-validation is a resampling procedure designed for sequentially ordered data. Instead of randomly partitioning observations — which would destroy temporal structure and introduce data leakage — it advances a forecast origin one step at a time, fitting a model on all past data up to that origin and evaluating it on the immediately following out-of-sample period. Economists, financial analysts, and meteorologists use it whenever an honest, operationally realistic estimate of predictive accuracy is required for a time-ordered process.