방법 비교

	Quandt-Andrews 검정 (알 수 없는 구조적 변화)	Bai-Perron 다중 구조 변동 검정	CUSUM 검정: 회귀 모형에서 모수 불안정성 탐지
분야	계량경제학	계량경제학	계량경제학
계열	Hypothesis test	Hypothesis test	Hypothesis test
기원 연도	1993	1998	1975
창시자	Donald Andrews	Jushan Bai & Pierre Perron	Brown, Durbin & Evans
유형	Supremum test for structural change	Sequential hypothesis test for multiple structural breaks	Recursive residual test
원전	Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821–856.	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	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.
별칭	sup-Wald Test, Andrews Breakpoint Test, Unknown Structural Break Test, Quandt Likelihood Ratio Test	Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi	Cumulative Sum Test, CUSUMSQ Test, Brown-Durbin-Evans Test, Kümülatif Toplam Testi
관련	3	2	3
요약	The Quandt-Andrews test, formalized by Andrews (1993), detects structural breaks in regression parameters when the breakpoint date is unknown a priori. It sweeps all candidate break dates within a trimmed interior of the sample, computes a Wald (or LM/LR) statistic at each candidate, and reports the supremum of those statistics. Applied economists and time-series analysts use it to test whether coefficients remain stable across a full estimation window without needing to specify when the break occurred.	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 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.