Comparar métodos

	Teste CD de Pesaran: Diagnóstico de Dependência Transversal para Dados em Painel	Teste LM de Breusch-Godfrey para Correlação Serial	Teste CIPS
Área	Econometria	Econometria	Econometria
Família	Hypothesis test	Regression model	Hypothesis test
Ano de origem	2021	1978	2007
Autor original	M. Hashem Pesaran	Trevor Breusch & Leslie Godfrey	M. Hashem Pesaran
Tipo	Non-parametric diagnostic test	Lagrange-multiplier test for serial correlation	Panel unit-root test with cross-section dependence
Fonte seminal	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.	Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.
Outros nomes	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	Pesaran CIPS Test, Cross-Sectionally Augmented IPS, Second-Generation Panel Unit-Root Test, CIPS Birim Kök Testi
Relacionados	3	3	3
Resumo	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 CIPS test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed for panels in which the cross-sectional units share unobserved common factors that induce cross-section dependence. By augmenting each individual ADF regression with cross-sectional averages and their lags, the CIPS test accounts for this dependence and produces reliable inference where first-generation tests such as the original IPS test break down. It is widely applied in macroeconomic and finance panels where shocks propagate across countries or regions.