পদ্ধতির তুলনা করুন

	স্ট্রাকচারাল ব্রেক এআরআইএমএ মডেল	ARIMA মডেল (অটোরিগ্রেসিভ ইন্টিগ্রেটেড মুভিং অ্যাভারেজ)	বাই-পেরন মাল্টিপল স্ট্রাকচারাল ব্রেক টেস্ট
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Hypothesis test
উদ্ভবের বছর	1989-1998	1970	1998
প্রবর্তক	Perron (1989); extended by Bai & Perron (1998)	George Box and Gwilym Jenkins	Jushan Bai & Pierre Perron
ধরন	Time series model with regime detection	Time series forecasting model	Sequential hypothesis test for multiple structural breaks
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	3	6	2
সারসংক্ষেপ	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.