Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application

By R. Scott Hacker, Abdulnasser Hatemi-J. 2006

650 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0be8a4e22771e03d17c815055fdf0c005b864b57

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In Economics and Econometrics

Citations in the corpus

The Causal Relationship Between Economic Policy Uncertainty and Stock Returns in China and India: Evidence from a Bootstrap Rolling Window Approach (2016, background): “…the outstanding performance of the RB test over standard asymptotic tests, regardless of cointegration properties, has been confirmed in the following Monte Carlo simulations studies (Mantalos and Shukur 1998; Mantalos 2000; Shukur and Mantalos 2000; Hacker and Hatemi-J 2006; Balcilar et al. 2010).”
Exploring Granger causality between global average observed time series of carbon dioxide and temperature (2011, methodology): “In case of potential violation of the classical statistical assumptions that are needed to justify the F test, Hacker and Hatemi-J (2006) suggests using a bootstrap distribution instead.”
The electricity consumption versus economic growth of the Polish economy (2012, neutral): “[23] Hacker, R.”
Small sample evidence on the tourism-led growth hypothesis in Lebanon (2014, methodology): “On the basis of the Monte Carlo experiment, Mantalos (2000) and Hacker and Hatemi-J (2006) found that the bootstrap tests improved the robustness of the causality test, particularly for a small sample.”
Exploring Causal Relationships in Visual Object Tracking (2015, methodology): “Although it has been revised and improved over the decades (e.g. Hacker and Hatemi-J [15] avoided the assumption of a normal distribution), Granger causality is suitable only for linear signals, since it is based on linear regression.”
Energy consumption and economic growth in the next 11 countries: The bootstrapped autoregressive metric causality approach (2014, background): “The last issue in this paper is the problem of a small sample since the standard asymptotic distribution theory may often cause significant over-rejection in the case of a small sample (Hacker and Hatemi-J, 2006; Lach, 2010).”