Salīdzināt metodes

	Hatemi-J asimetriskās koincidences tests	Toda-Yamamoto (TY) Granger cēloņsakarības pārbaude
Nozare	Ekonometrija	Ekonometrija
Saime	Hypothesis test	Hypothesis test
Izcelsmes gads	2012	1995
Autors	Abdulnasser Hatemi-J	Hiro Toda & Taku Yamamoto
Tips	Nonlinear Granger causality test	Modified Wald test on augmented VAR
Pirmavots	Hatemi-J, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43(1), 447–456.	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250.
Citi nosaukumi	Hatemi-J Asymmetric Causality Test, Asymmetric Causality Test, Positive and Negative Causality Test, Asimetrik Nedensellik Testi	TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi
Saistītās	3	3
Kopsavilkums	The Hatemi-J asymmetric causality test, introduced by Abdulnasser Hatemi-J in 2012, extends the Granger causality framework to allow causal relationships between the positive and negative components of integrated time series to differ. By decomposing each series into cumulative positive and negative partial sums and embedding the Toda-Yamamoto approach within a VAR, the test enables researchers to distinguish whether positive shocks, negative shocks, or both drive causation between economic variables.	The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic.