Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo autorregresivo (AR)× | Prueba de Causalidad de Granger× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1970s (popularised 1976) | 1969 |
| Autor original≠ | George E. P. Box and Gwilym M. Jenkins | Clive W. J. Granger |
| Tipo≠ | Time series model | Causality test (F-test on VAR) |
| Fuente seminal≠ | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| Alias | AR model, AR(p) model, autoregression, AR process | Granger test, GC test, predictive causality test, Granger non-causality test |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. | The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis. |
| ScholarGateConjunto de datos ↗ |
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