Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo ARIMA con Roturas Estructurales× | Modelo ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1989-1998 | 1970 |
| Autor original≠ | Perron (1989); extended by Bai & Perron (1998) | George Box and Gwilym Jenkins |
| Tipo≠ | Time series model with regime detection | Time series forecasting model |
| Fuente seminal≠ | 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 ↗ |
| Alias | ARIMA with structural breaks, break-adjusted ARIMA, piecewise ARIMA, ARIMA with regime shifts | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Relacionados≠ | 3 | 6 |
| Resumen≠ | A structural break ARIMA model extends the standard ARIMA framework by explicitly identifying and accommodating one or more abrupt shifts in the level, trend, or dynamics of a time series. Rather than forcing a single set of ARIMA parameters across the entire sample, it fits separate ARIMA specifications for each regime defined by the detected break dates. | 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. |
| ScholarGateConjunto de datos ↗ |
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