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| Uji Kausalitas Granger Nonlinear× | Uji Batas ARDL Nonlinear (NARDL)× | |
|---|---|---|
| Bidang | Ekonometrika | Ekonometrika |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1992-2006 | 2014 |
| Pencetus≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Shin, Yu, and Greenwood-Nimmo |
| Tipe≠ | Nonparametric causality test | Asymmetric cointegration test |
| Sumber perintis≠ | Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669. DOI ↗ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281-314). Springer. DOI ↗ |
| Alias | nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality | NARDL, asymmetric ARDL, nonlinear bounds testing approach, NARDL bounds testing |
| Terkait≠ | 6 | 1 |
| Ringkasan≠ | Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture. | The Nonlinear ARDL bounds test, developed by Shin, Yu, and Greenwood-Nimmo (2014), extends the linear ARDL framework to detect asymmetric long-run relationships in time series. By decomposing a regressor into positive and negative partial sums, NARDL simultaneously tests for cointegration and estimates separate long-run effects for increases and decreases — without requiring all variables to be integrated of the same order. |
| ScholarGateSet data ↗ |
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