विधियों की तुलना करें

	ऑटोरेग्रेसिव इंटीग्रेटेड मूविंग एवरेज (ARIMA) मॉडल	बाई-पेरॉन मल्टीपल स्ट्रक्चरल ब्रेक टेस्ट	संरचनात्मक विच्छेद के लिए चाउ परीक्षण
क्षेत्र	अर्थमिति	अर्थमिति	अर्थमिति
परिवार	Regression model	Hypothesis test	Regression model
उद्भव वर्ष	1970	1998	1960
प्रवर्तक	George Box and Gwilym Jenkins	Jushan Bai & Pierre Perron	Gregory C. Chow
प्रकार	Time series forecasting model	Sequential hypothesis test for multiple structural breaks	Test for structural break in regression coefficients
मौलिक स्रोत	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	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.
उपनाम	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	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
संबंधित	6	2	2
सारांश	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	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.