Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| ARDL-rajoitustestin rakenteellinen muutos× | ARDL-raja-testi (Pesaranin raja-testi)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2001–2010s | 2001 |
| Kehittäjä≠ | Pesaran, Shin & Smith (bounds framework); structural break extensions by Bahmani-Oskooee, Enders & Jones, and others | Pesaran, Shin & Smith |
| Tyyppi≠ | Cointegration / bounds test | Cointegration test / Autoregressive distributed lag model |
| Alkuperäislähde | 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 ↗ |
| Rinnakkaisnimet | SB-ARDL bounds test, ARDL bounds test with structural break, Fourier ARDL bounds test, break-augmented bounds testing | Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test) |
| Liittyvät≠ | 6 | 4 |
| Tiivistelmä≠ | The structural break ARDL bounds test extends the Pesaran, Shin and Smith (2001) bounds testing framework to accommodate one or more structural breaks in the long-run relationship between time-series variables. By incorporating break dummies or smooth Fourier terms into the ARDL error-correction equation, it allows researchers to test for cointegration even when the data have experienced shifts in intercept or slope caused by policy changes, crises, or regime switches. | 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. |
| ScholarGateAineisto ↗ |
|
|