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	Test niezależności warunkowej Giacominiego-White'a	Regresja krokowa
Dziedzina	Ekonometria	Statystyka
Rodzina	Hypothesis test	Regression model
Rok powstania	2006	1960
Twórca	Raffaella Giacomini & Halbert White	M. A. Efroymson
Typ	Non-nested forecast comparison test	Automated variable selection
Źródło pierwotne	Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.	Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley.
Inne nazwy	GW Test, Conditional Predictive Ability Test, Giacomini-White CPA Test, Koşullu Tahmin Yeteneği Testi	stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection
Pokrewne	3	5
Podsumowanie	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.	Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library.