Сравнение на методи

	Тест на Дюмитреску-Хюрлин за панелна причинност на Грейнджър	Тест за причинност на Грейнджър	Модел с фиксирани ефекти за панелни данни
Област	Иконометрия	Иконометрия	Иконометрия
Семейство	Hypothesis test	Regression model	Regression model
Година на възникване	2012	1969	2014
Създател	Elena-Ivona Dumitrescu & Christophe Hurlin	Clive W. J. Granger	Hsiao (textbook treatment); within transformation of panel data
Тип	Non-causality test for heterogeneous panels	Time-series predictive causality test	Panel data regression
Основополагащ източник	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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Други названия	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	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Свързани	3	5	5
Резюме	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 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).