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EMPIRICAL LIKELIHOOD TEST FOR CAUSALITY OF BIVARIATE AR(1) PROCESSES

Published online by Cambridge University Press:  10 October 2013

D. Li ,
N. H. Chan  and
L. Peng
D. Li*
Affiliation:
Fudan University
N. H. Chan
Affiliation:
Chinese University of Hong Kong and Renmin University of China
L. Peng
Affiliation:
Georgia Institute of Technology
*
*Address correspondence to D. Li, Department of Statistics, School of Management, Fudan University, 670 Guoshun Road, Shanghai 200433, P.R. China; e-mail: deyuanli@fudan.edu.cn.
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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.

Type
ARTICLES
Information
Econometric Theory , Volume 30 , Issue 2 , April 2014 , pp. 357 - 371
Copyright
Copyright © Cambridge University Press 2013 

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References

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