Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Njia ya Kufuatilia Kiotomatiki ya Kuingiliana Isiyo ya Laini (NARDL)× | Regression ya Kiasi (Quantile Regression)× | System GMM (Arellano-Bover / Blundell-Bond)× | |
|---|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model | Regression model |
| Mwaka wa asili≠ | 2014 | 1978 | 1998 |
| Mwanzilishi≠ | Shin, Yu & Greenwood-Nimmo | Koenker & Bassett | Arellano & Bover (1995); Blundell & Bond (1998) |
| Aina≠ | Asymmetric cointegration / error-correction model | Conditional quantile regression | Dynamic panel data estimator |
| Chanzo asilia≠ | Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. Springer. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ | Arellano, M. & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277-297. DOI ↗ |
| Majina mbadala≠ | nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL) | conditional quantile regression, regression quantiles, Kantil Regresyon | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) |
| Zinazohusiana≠ | 4 | 5 | 4 |
| Muhtasari≠ | The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. | System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small. |
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