Comparar métodos
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| Prueba CD de Pesaran: Diagnóstico de Dependencia Transversal para Datos de Panel× | Contraste LM de Breusch-Godfrey para autocorrelación serial× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia≠ | Hypothesis test | Regression model |
| Año de origen≠ | 2021 | 1978 |
| Autor original≠ | M. Hashem Pesaran | Trevor Breusch & Leslie Godfrey |
| Tipo≠ | Non-parametric diagnostic test | Lagrange-multiplier test for serial correlation |
| Fuente seminal≠ | Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50. DOI ↗ | Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46(6), 1293–1301. DOI ↗ |
| Alias | CD Test, Cross-Sectional Dependence Test, Pesaran General CD Test, Kesitsel Bağımlılık Testi | BG test, LM test for autocorrelation, Breusch-Godfrey serial correlation test, Breusch-Godfrey otokorelasyon testi |
| Relacionados | 3 | 3 |
| Resumen≠ | The Pesaran CD test is a general diagnostic procedure for detecting cross-sectional dependence in panel data models. Developed by M. Hashem Pesaran (2021), it is applicable to both balanced and unbalanced panels with large N and T, and retains validity under heterogeneous slope coefficients. The test is widely adopted in empirical economics, finance, and political economy as a prerequisite check before selecting appropriate estimators or unit-root tests for panel datasets. | The Breusch-Godfrey test is a Lagrange-multiplier test for serial correlation in regression residuals, developed independently by Trevor Breusch (1978) and Leslie Godfrey (1978). Unlike the Durbin-Watson test, it detects autocorrelation up to any chosen order p, remains valid when the model includes lagged dependent variables, and produces a definite chi-square p-value rather than an inconclusive region — making it the modern standard for autocorrelation testing. |
| ScholarGateConjunto de datos ↗ |
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