Сравнение методов

	OLS со структурными разрывами	Модель ARIMA (авторегрессионная интегрированная скользящая средняя)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1960–1998	1970
Автор метода	Chow (1960) for the breakpoint test; Bai & Perron (1998) for multiple break estimation	George Box and Gwilym Jenkins
Тип	Segmented linear regression	Time series forecasting model
Основополагающий источник	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.
Другие названия	OLS with structural breaks, piecewise OLS, regime-switching OLS, breakpoint regression	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Связанные	6	6
Сводка	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.