Bandingkan kaedah

	Ujian Kausaliti Granger Dolado-Lütkepohl	Ujian Kausaliti Granger	Model Regresi Autoruang (VAR)
Bidang	Ekonometrik	Ekonometrik	Ekonometrik
Keluarga	Hypothesis test	Regression model	Regression model
Tahun asal	1996	1969	2005
Pengasas	Juan Dolado & Helmut Lütkepohl	Clive W. J. Granger	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Jenis	Modified Wald test for Granger causality in possibly integrated or cointegrated VAR systems	Time-series predictive causality test	Multivariate time-series model
Sumber perintis	Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369–386.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	DL Causality Test, Modified Wald Causality Test, Augmented VAR Causality Test, Dolado-Lütkepohl Testi	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Berkaitan	2	5	4
Ringkasan	The Dolado-Lütkepohl (DL) test, introduced by Dolado and Lütkepohl (1996), is a modified Wald procedure for testing Granger causality in vector autoregressive (VAR) systems whose variables may be integrated or cointegrated. By fitting a VAR of slightly higher order than necessary and restricting the Wald statistic to the first p lag blocks, the test recovers the standard chi-squared limiting distribution without requiring pre-testing for cointegration or transformation to error-correction form.	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.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).