Porovnat metody

	Test CUSUM: Detekce nestability parametrů v regresních modelech	Bai-Perronův test vícenásobných strukturálních zlomů	Chowův test strukturální změny
Obor	Ekonometrie	Ekonometrie	Ekonometrie
Rodina	Hypothesis test	Hypothesis test	Regression model
Rok vzniku	1975	1998	1960
Tvůrce	Brown, Durbin & Evans	Jushan Bai & Pierre Perron	Gregory C. Chow
Typ	Recursive residual test	Sequential hypothesis test for multiple structural breaks	Test for structural break in regression coefficients
Původní zdroj	Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B, 37(2), 149–192.	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.
Další názvy	Cumulative Sum Test, CUSUMSQ Test, Brown-Durbin-Evans Test, Kümülatif Toplam Testi	Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi	Chow breakpoint test, structural break test, Chow yapısal kırılma testi
Příbuzné	3	2	2
Shrnutí	The CUSUM (Cumulative Sum) and CUSUMSQ (Cumulative Sum of Squares) tests, introduced by Brown, Durbin, and Evans (1975), assess whether the coefficients of a linear regression model remain constant over time. They are standard tools in econometrics for detecting structural breaks, policy shifts, or regime changes in time-series data without requiring prior knowledge of when a break occurs.	The Bai-Perron test, introduced by Jushan Bai and Pierre Perron in their landmark 1998 Econometrica paper, is a least-squares-based procedure for detecting, estimating, and testing the number of structural breaks in a linear regression model estimated on time-series data. Unlike single-break tests, it simultaneously identifies multiple change-points in a sample, providing economists and empirical researchers with a rigorous, data-driven way to locate parameter instability across time.	The Chow test, introduced by Gregory Chow in 1960, checks whether the coefficients of a linear regression are the same across two subsamples — that is, whether a structural break occurs at a known point such as a policy change, crisis, or regime shift. It compares the fit of a single pooled regression with the combined fit of two separate regressions; a large improvement from splitting indicates the relationship differs between the two periods or groups.