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Empirical Likelihood Test For Causality Of Bivariate Ar(1) Processes

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  • Li, D.
  • Chan, N. H.
  • Peng, L.

Abstract

Testing for causality is of critical importance for many econometric applications. For bivariate AR(1) processes, the limit distributions of causality tests based on least squares estimation depend on the presence of nonstationary processes. When nonstationary processes are present, the limit distributions of such tests are usually very complicated, and the full-sample bootstrap method becomes inconsistent as pointed out in Choi (2005, Statistics and Probability Letters 75, 39–48). In this paper, a profile empirical likelihood method is proposed to test for causality. The proposed test statistic is robust against the presence of nonstationary processes in the sense that one does not have to determine the existence of nonstationary processes a priori. Simulation studies confirm that the proposed test statistic works well.

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  • Li, D. & Chan, N. H. & Peng, L., 2014. "Empirical Likelihood Test For Causality Of Bivariate Ar(1) Processes," Econometric Theory, Cambridge University Press, vol. 30(2), pages 357-371, April.
    • Handle: RePEc:cup:etheor:v:30:y:2014:i:02:p:357-371_00
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    Cited by:

    1. Xiaohui Liu & Yuzi Liu & Yao Rao & Fucai Lu, 2021. "A Unified test for the Intercept of a Predictive Regression Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 571-588, April.
    2. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.

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