So sánh phương pháp

	Phương pháp Kónya Bootstrap Panel Granger Causality	Kiểm định nhân quả Granger	Kiểm định Pesaran CD: Chẩn đoán sự phụ thuộc chéo cho dữ liệu bảng
Lĩnh vực	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng
Họ	Hypothesis test	Regression model	Hypothesis test
Năm ra đời	2006	1969	2021
Người khởi xướng	László Kónya	Clive W. J. Granger	M. Hashem Pesaran
Loại	Non-parametric bootstrap hypothesis test	Time-series predictive causality test	Non-parametric diagnostic test
Công trình gốc	Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992.	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.
Tên gọi khác	Bootstrap Panel Causality Test, Kónya Panel Granger Causality, SUR-Based Bootstrap Causality, Kónya Önyükleme 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
Liên quan	3	5	3
Tóm tắt	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 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.