Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo ARIMA (Autoregressive Integrated Moving Average)× | Modelo EGARCH (GARCH Exponencial)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1970 | 1991 |
| Autor original≠ | George Box and Gwilym Jenkins | Daniel B. Nelson |
| Tipo≠ | Time series forecasting model | Volatility / conditional variance model |
| Fuente seminal≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Alias | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Relacionados | 6 | 6 |
| Resumen≠ | 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. | The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets. |
| ScholarGateConjunto de datos ↗ |
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