Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Ikke-lineær autoregressiv distribuert lag (NARDL) modell× | Minste kvadraters metode (OLS)× | |
|---|---|---|
| Fagfelt | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 2014 | 2019 |
| Opphavsperson≠ | Shin, Yu & Greenwood-Nimmo | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Asymmetric cointegration / error-correction model | Linear regression |
| Opprinnelig kilde≠ | 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 |
| Alias≠ | nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relaterte≠ | 4 | 5 |
| Sammendrag≠ | 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). |
| ScholarGateDatasett ↗ |
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