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| OLS с отчитане на структурни прекъсвания× | Модел ARIMA (Авторегресионен интегриран плъзгащ се среден)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1960–1998 | 1970 |
| Създател≠ | 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 |
| Основополагащ източник≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Други названия | OLS with structural breaks, piecewise OLS, regime-switching OLS, breakpoint regression | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Свързани | 6 | 6 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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