Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo ARIMA de Parámetros Variables en el Tiempo (TVP-ARIMA)× | Modelo ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1976–1989 | 1970 |
| Autor original≠ | Cooley & Prescott (1976); Harvey (1989) state-space formulation | George Box and Gwilym Jenkins |
| Tipo≠ | Time series model with evolving coefficients | Time series forecasting model |
| Fuente seminal≠ | Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521405737 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | TVP-ARIMA, time-varying ARIMA, adaptive ARIMA, state-space ARIMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Relacionados≠ | 3 | 6 |
| Resumen≠ | The time-varying parameter ARIMA model extends the classical ARIMA framework by allowing its autoregressive and moving-average coefficients to evolve over time rather than remaining fixed. Cast in state-space form and estimated via the Kalman filter, it is designed for economic and financial time series whose dynamic structure shifts in response to structural breaks, policy changes, or regime transitions. | 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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