Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Testul de Cointegrare Engle-Granger× | Model ARIMA (Autoregresiv Integrat Medie Mobilă)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1987 | 1970 |
| Autorul original≠ | Robert F. Engle and Clive W. J. Granger | George Box and Gwilym Jenkins |
| Tip≠ | Cointegration test | Time series forecasting model |
| Sursa seminală≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Denumiri alternative | EG cointegration test, Engle-Granger two-step method, residual-based cointegration test, EG test | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Înrudite≠ | 5 | 6 |
| Rezumat≠ | The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment. | 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. |
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