手法を比較

	構造的ブレークOLS	自己回帰和分移動平均モデル (ARIMA Model)
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	1960–1998	1970
提唱者	Chow (1960) for the breakpoint test; Bai & Perron (1998) for multiple break estimation	George Box and Gwilym Jenkins
種類	Segmented linear regression	Time series forecasting model
原典	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.
別名	OLS with structural breaks, piecewise OLS, regime-switching OLS, breakpoint regression	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
関連	6	6
概要	Structural Break OLS extends ordinary least squares to allow regression coefficients to shift at one or more breakpoints in time or across regimes. Rather than forcing a single coefficient vector across the entire sample, the model partitions the data and estimates a separate OLS regression within each segment, making it appropriate when economic relationships are suspected to change due to policy shifts, crises, or other structural events.	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.