Porównaj metody

	Test Pesaran CD: Diagnostyka zależności przekrojowej dla danych panelowych	Test LM Breuscha-Godfreya na autokorelację szeregową	Test niezależności przekrojowej Freesa dla danych panelowych
Dziedzina	Ekonometria	Ekonometria	Ekonometria
Rodzina	Hypothesis test	Regression model	Hypothesis test
Rok powstania	2021	1978	1995
Twórca	M. Hashem Pesaran	Trevor Breusch & Leslie Godfrey	Edward Frees
Typ	Non-parametric diagnostic test	Lagrange-multiplier test for serial correlation	Non-parametric panel diagnostic test
Źródło pierwotne	Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50.	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.	Frees, E. W. (1995). Assessing cross-sectional correlation in panel data. Journal of Econometrics, 69(2), 393–414.
Inne nazwy	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	Frees CD Test, Frees Q-statistic Test, Cross-Sectional Dependence Test (Frees), Frees Bağımlılık Testi
Pokrewne	3	3	3
Podsumowanie	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.	The Frees test, introduced by Edward Frees in 1995, is a non-parametric diagnostic procedure for detecting cross-sectional dependence in panel data. It is designed for settings where N (number of units) is large and T (time periods) is moderate, making it a standard pre-estimation check before applying panel regression methods that assume cross-sectional independence. Applied economists and social scientists routinely use it to verify whether units in the panel share common shocks or spatial linkages.