Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo Autorregresivo Robusto× | Modelo ARMA (Autoregresivo de Media Móvil)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1986 | 1970 |
| Autor original≠ | Martin & Yohai (influential early work); broader robust time series literature | George E. P. Box and Gwilym M. Jenkins |
| Tipo≠ | Robust time series model | Time series model |
| Fuente seminal≠ | Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | The robust AR model fits an autoregressive time series specification using estimation methods — typically M-estimators or bounded-influence estimators — that resist distortion from outliers and heavy-tailed error distributions. Unlike OLS-based AR estimation, robust variants down-weight extreme observations so that a small number of contaminated data points cannot dominate the fitted dynamics. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
| ScholarGateConjunto de datos ↗ |
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