Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский ARDL-тест на коинтеграцию× | Тест границ ARDL (Pesaran Bounds Test)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2001 (ARDL); Bayesian extension 2010s | 2001 |
| Автор метода≠ | Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature | Pesaran, Shin & Smith |
| Тип≠ | Cointegration / bounds testing | Cointegration test / Autoregressive distributed lag model |
| Основополагающий источник≠ | 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 ↗ | 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 | Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test | Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test) |
| Связанные≠ | 5 | 4 |
| Сводка≠ | The Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one. | 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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