Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель нелинейной авторегрессии с распределенным лагом (NARDL)× | Тест границ ARDL (Pesaran Bounds Test)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2014 | 2001 |
| Автор метода≠ | Shin, Yu & Greenwood-Nimmo | Pesaran, Shin & Smith |
| Тип≠ | Nonlinear cointegration model | Cointegration test / Autoregressive distributed lag model |
| Основополагающий источник≠ | 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. link ↗ | Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326. DOI ↗ |
| Другие названия | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model | Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test) |
| Связанные≠ | 5 | 4 |
| Сводка≠ | The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically. | The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations. |
| ScholarGateНабор данных ↗ |
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