手法を比較

	構造的ブレークSARIMAモデル	Bai-Perron 複数構造切断検定	SARIMAモデル
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Hypothesis test	Regression model
提唱年	1970s–1998	1998	1970 (first edition); 1976 (revised)
提唱者	Box & Jenkins (SARIMA); Bai & Perron (structural break detection)	Jushan Bai & Pierre Perron	Box, Jenkins, and Reinsel
種類	Time series model with regime shifts	Sequential hypothesis test for multiple structural breaks	Seasonal time series model
原典	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	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., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744
別名	SARIMA with structural breaks, break-augmented SARIMA, piecewise SARIMA, SARIMA-SB	Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi	SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
関連	3	2	5
概要	The Structural Break SARIMA model extends the classical Seasonal ARIMA framework by explicitly detecting and accommodating abrupt, permanent shifts in the level, trend, or seasonal pattern of a time series. Rather than forcing a single SARIMA specification across the entire sample, the model partitions the series at estimated breakpoints and fits separate SARIMA processes to each resulting segment, producing more accurate forecasts and reliable inference in the presence of regime changes.	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.	SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics.