Empirical likelihood test for causality of bivariate AR(1) processes

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)357-371
Journal / PublicationEconometric Theory
Volume30
Issue number2
Publication statusPublished - 29 Nov 2014
Externally publishedYes

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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Citation Format(s)

Empirical likelihood test for causality of bivariate AR(1) processes. / Li, D.; Chan, N. H.; Peng, L.
In: Econometric Theory, Vol. 30, No. 2, 29.11.2014, p. 357-371.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review