Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Нелинеен авторегресивен модел с разпределени лагове (NARDL)× | Метод на най-малките квадрати (МНК)× | Квантилна регресия× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2014 | 2019 | 1978 |
| Създател≠ | Shin, Yu & Greenwood-Nimmo | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| Тип≠ | Asymmetric cointegration / error-correction model | Linear regression | Conditional quantile regression |
| Основополагащ източник≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Други названия≠ | nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Свързани≠ | 4 | 5 | 5 |
| Резюме≠ | 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. | 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). | 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. |
| ScholarGateНабор от данни ↗ |
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