Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modelis Nardl (Nonlinear Autoregressive Distributed Lag Model)× | Grindžera koeficientu pārbaude× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2014 | 1969 |
| Autors≠ | Shin, Yu, and Greenwood-Nimmo | Clive W. J. Granger |
| Tips≠ | Nonlinear cointegration model | Time-series predictive causality test |
| Pirmavots≠ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281-314). Springer. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ |
| Citi nosaukumi | NARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds test | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing an explanatory variable into its positive and negative partial sums, it tests whether increases and decreases in a regressor have different effects on the dependent variable — a feature that linear cointegration methods cannot capture. | The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. |
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