手法を比較

	Granger因果性検定	Kónya Bootstrap Panel Granger Causality	パネルデータ固定効果モデル
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Hypothesis test	Regression model
提唱年	1969	2006	2014
提唱者	Clive W. J. Granger	László Kónya	Hsiao (textbook treatment); within transformation of panel data
種類	Time-series predictive causality test	Non-parametric bootstrap hypothesis test	Panel data regression
原典	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	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.
別名	Granger causality test, Granger non-causality test, predictive causality test, Granger 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
関連	5	3	5
概要	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.	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).