เปรียบเทียบวิธี

	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	การทดสอบการเปลี่ยนแปลงโครงสร้างพหุคูณของ Bai-Perron
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Hypothesis test
ปีกำเนิด	1970	1998
ผู้ริเริ่ม	George Box and Gwilym Jenkins	Jushan Bai & Pierre Perron
ประเภท	Time series forecasting model	Sequential hypothesis test for multiple structural breaks
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	6	2
สรุป	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.