Võrdle meetodeid

	Driscoll-Kraay standardvead	Newey-West HAC standardvead	Pesaran CD Test
Valdkond	Ökonomeetria	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model	Hypothesis test
Tekkeaasta	1998	1987	2021
Looja	John Driscoll & Aart Kraay	Whitney Newey & Kenneth West	M. Hashem Pesaran
Tüüp	Nonparametric heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator for panel data	Covariance matrix estimator	Non-parametric diagnostic test
Algallikas	Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560.	Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.	Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50.
Rööpnimetused	DK Standard Errors, Driscoll-Kraay Covariance Estimator, Spatial-Temporal HAC Standard Errors, Driscoll-Kraay Standart Hatalar	HAC standard errors, Heteroskedasticity and Autocorrelation Consistent covariance, Bartlett kernel HAC estimator, HAC düzeltmeli standart hatalar	CD Test, Cross-Sectional Dependence Test, Pesaran General CD Test, Kesitsel Bağımlılık Testi
Seotud	2	1	3
Kokkuvõte	Driscoll-Kraay standard errors provide a nonparametric, heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator for balanced and unbalanced panel datasets. Introduced by Driscoll and Kraay in 1998, the method corrects inference when residuals exhibit cross-sectional dependence, serial autocorrelation, and heteroskedasticity simultaneously—problems common in macroeconomic and international finance panels where units such as countries or industries share common shocks.	Newey-West HAC standard errors, introduced by Whitney Newey and Kenneth West in 1987, provide a covariance matrix estimator for OLS regression that remains valid under both heteroskedasticity and serial autocorrelation of unknown form. They are the standard tool for correcting inference in time-series and panel regression when residuals are not i.i.d., requiring no specification of the error structure beyond choosing a bandwidth parameter.	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.