Сравнение на методи
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| Нелинеен авторегресивен модел с разпределени лагове (NARDL)× | Модел на авторегресия с плавен преход (STAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2014 | 1994 |
| Създател≠ | Shin, Yu & Greenwood-Nimmo | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) |
| Тип≠ | Asymmetric cointegration / error-correction model | Nonlinear time-series regime-switching model |
| 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 ↗ | Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗ |
| Други названия≠ | nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL) | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR |
| Свързани | 4 | 4 |
| 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. | The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations. |
| ScholarGateНабор от данни ↗ |
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