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| Нелинеен модел на авторегресивни разпределени лагове (NARDL)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2014 | 2019 |
| Създател≠ | Shin, Yu, and Greenwood-Nimmo | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Nonlinear cointegration model | Linear regression |
| Основополагащ източник≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия | NARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds test | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateНабор от данни ↗ |
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