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| ARDL Sezionale Trasversale× | Test di Cointegrazione di Maki× | Panel DF-GLS× | |
|---|---|---|---|
| Campo | Econometria | Econometria | Econometria |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 2006 | 2012 | 1996 |
| Ideatore≠ | Pesaran and colleagues | Darshana Maki | Elliott, Rothenberg, and Stock (adapted to panels) |
| Tipo≠ | Dynamic panel model | Structural-break test | Stationarity test |
| Fonte seminale≠ | Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117. link ↗ | Maki, D. (2012). Tests for cointegration allowing for an unknown number of breaks. Economic Modelling, 29(5), 2011-2015. DOI ↗ | Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometric Reviews, 13(4), 469-497. DOI ↗ |
| Alias | Panel ARDL with cross-sectional dependence | Structural-break cointegration test | Panel unit-root test |
| Correlati | 3 | 3 | 3 |
| Sintesi≠ | CS-ARDL (Cross-Sectional ARDL) applies the ARDL framework to panel data while explicitly accounting for cross-sectional dependence—correlation of shocks and relationships across units (countries, firms, regions). Introduced by Pesaran and colleagues (2016), it extends panel ARDL methods to handle common factors or global shocks affecting all units simultaneously. This is crucial for realistic modeling of internationally integrated economies and firm networks. | The Maki cointegration test extends cointegration testing to allow for an unknown number of endogenously-determined structural breaks in the cointegrating relationship. Introduced by Maki (2012), it builds on Gregory and Hansen (1996), enabling detection of cointegration even when relationships shift due to policy changes, institutional reforms, or fundamental regime shifts. This is essential for applied time-series work where structural change is common. | Panel DF-GLS extends the Elliott, Rothenberg, and Stock (1996) GLS unit-root test to panel data, combining cross-sectional and time-series information to test whether variables contain unit roots. Introduced by Hadri and colleagues (2005), it is more powerful than standard panel unit-root tests (IPS, LLC) due to its GLS detrending approach. This test is essential for establishing stationarity before fitting cointegration or dynamic panel models. |
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