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| Test di Cointegrazione di Maki× | ARDL Sezionale Trasversale× | Panel DF-GLS× | |
|---|---|---|---|
| Campo | Econometria | Econometria | Econometria |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 2012 | 2006 | 1996 |
| Ideatore≠ | Darshana Maki | Pesaran and colleagues | Elliott, Rothenberg, and Stock (adapted to panels) |
| Tipo≠ | Structural-break test | Dynamic panel model | Stationarity test |
| Fonte seminale≠ | Maki, D. (2012). Tests for cointegration allowing for an unknown number of breaks. Economic Modelling, 29(5), 2011-2015. DOI ↗ | Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117. link ↗ | Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometric Reviews, 13(4), 469-497. DOI ↗ |
| Alias | Structural-break cointegration test | Panel ARDL with cross-sectional dependence | Panel unit-root test |
| Correlati | 3 | 3 | 3 |
| Sintesi≠ | 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. | 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. | 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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