Võrdle meetodeid

	Struktuurimuutusega ARIMA mudel	ARIMA mudel (autoregressiivne integreeritud libisev keskmine)	Bai-Perroni mitme struktuurimurde test
Valdkond	Ökonomeetria	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model	Hypothesis test
Tekkeaasta	1989-1998	1970	1998
Looja	Perron (1989); extended by Bai & Perron (1998)	George Box and Gwilym Jenkins	Jushan Bai & Pierre Perron
Tüüp	Time series model with regime detection	Time series forecasting model	Sequential hypothesis test for multiple structural breaks
Algallikas	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.	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.
Rööpnimetused	ARIMA with structural breaks, break-adjusted ARIMA, piecewise ARIMA, ARIMA with regime shifts	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
Seotud	3	6	2
Kokkuvõte	A structural break ARIMA model extends the standard ARIMA framework by explicitly identifying and accommodating one or more abrupt shifts in the level, trend, or dynamics of a time series. Rather than forcing a single set of ARIMA parameters across the entire sample, it fits separate ARIMA specifications for each regime defined by the detected break dates.	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.