Σύγκριση μεθόδων

	Δοκιμή Αιτιότητας Granger Πινάκων Dumitrescu-Hurlin	Δοκιμή Αιτιότητας Granger	Δοκιμή Pesaran CD: Διαγνωστικός Έλεγχος Εξάρτησης Διατομών για Δεδομένα Πάνελ
Πεδίο	Οικονομετρία	Οικονομετρία	Οικονομετρία
Οικογένεια	Hypothesis test	Regression model	Hypothesis test
Έτος προέλευσης	2012	1969	2021
Δημιουργός	Elena-Ivona Dumitrescu & Christophe Hurlin	Clive W. J. Granger	M. Hashem Pesaran
Τύπος	Non-causality test for heterogeneous panels	Time-series predictive causality test	Non-parametric diagnostic test
Θεμελιώδης πηγή	Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50.
Εναλλακτικές ονομασίες	DH Causality Test, Panel Granger Causality Test (Heterogeneous), Dumitrescu-Hurlin Test, Heterojen Panel Nedensellik Testi	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	CD Test, Cross-Sectional Dependence Test, Pesaran General CD Test, Kesitsel Bağımlılık Testi
Συναφείς	3	5	3
Σύνοψη	The Dumitrescu-Hurlin (DH) test, introduced by Elena-Ivona Dumitrescu and Christophe Hurlin in their 2012 Economic Modelling article, tests for Granger non-causality in heterogeneous panel datasets. Unlike standard panel causality approaches, it permits each cross-sectional unit to have its own distinct causal relationship, making it well-suited for macro-panels of countries, firms, or regions where homogeneity cannot be assumed.	The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.	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.