Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model ARIMA (Autoregresiv Integrat Medie Mobilă)× | Model EGARCH (Exponential GARCH)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1970 | 1991 |
| Autorul original≠ | George Box and Gwilym Jenkins | Daniel B. Nelson |
| Tip≠ | Time series forecasting model | Volatility / conditional variance model |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
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