手法を比較

	Kónya Bootstrap Panel Granger Causality	デュミトレスク・フルリンパネルグレンジャー因果性検定	Granger因果性検定
分野	計量経済学	計量経済学	計量経済学
系統	Hypothesis test	Hypothesis test	Regression model
提唱年	2006	2012	1969
提唱者	László Kónya	Elena-Ivona Dumitrescu & Christophe Hurlin	Clive W. J. Granger
種類	Non-parametric bootstrap hypothesis test	Non-causality test for heterogeneous panels	Time-series predictive causality test
原典	Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992.	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.
別名	Bootstrap Panel Causality Test, Kónya Panel Granger Causality, SUR-Based Bootstrap Causality, Kónya Önyükleme Nedensellik Testi	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
関連	3	3	5
概要	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 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.