Bandingkan metode

	Uji CUSUM: Mendeteksi Ketidakstabilan Parameter dalam Model Regresi	Uji Bai-Perron Berganda untuk Perubahan Struktural	Uji Quandt-Andrews untuk Perubahan Struktural yang Tidak Diketahui
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Hypothesis test	Hypothesis test	Hypothesis test
Tahun asal	1975	1998	1993
Pencetus	Brown, Durbin & Evans	Jushan Bai & Pierre Perron	Donald Andrews
Tipe	Recursive residual test	Sequential hypothesis test for multiple structural breaks	Supremum test for structural change
Sumber perintis	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.	Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821–856.
Alias	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	sup-Wald Test, Andrews Breakpoint Test, Unknown Structural Break Test, Quandt Likelihood Ratio Test
Terkait	3	2	3
Ringkasan	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 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.