Confronta i metodi

	Test di Causalità di Granger per Pannelli di Dumitrescu-Hurlin	Causalità di Granger Bootstrap di Kónya	Modello a Effetti Fissi per Dati Panel
Campo	Econometria	Econometria	Econometria
Famiglia	Hypothesis test	Hypothesis test	Regression model
Anno di origine	2012	2006	2014
Ideatore	Elena-Ivona Dumitrescu & Christophe Hurlin	László Kónya	Hsiao (textbook treatment); within transformation of panel data
Tipo	Non-causality test for heterogeneous panels	Non-parametric bootstrap hypothesis test	Panel data regression
Fonte seminale	Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.	Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alias	DH Causality Test, Panel Granger Causality Test (Heterogeneous), Dumitrescu-Hurlin Test, Heterojen Panel Nedensellik Testi	Bootstrap Panel Causality Test, Kónya Panel Granger Causality, SUR-Based Bootstrap Causality, Kónya Önyükleme Nedensellik Testi	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Correlati	3	3	5
Sintesi	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.	Introduced by László Kónya in 2006, this method tests Granger causality in heterogeneous panels by estimating a Seemingly Unrelated Regressions (SUR) system and deriving country-specific critical values through bootstrapping. Unlike pooled panel tests, it delivers a separate causality verdict for each cross-section, making it particularly valuable in applied macroeconomics and international economics when panel units are expected to behave differently.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).