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Nonparametric Test for Causality with Long-Range Dependence - (Now published in Econometrica, 68, (2000) pp.1465-1490

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  • Javier Hidalgo

Abstract

This paper introduces a nonparametric Granger-causality test for covariance stationary linear processes under, possibly, the presence of long-range dependence. We show that the test is consistent and has power against contiguous alternatives converging to the parametric rate T-½. Since the test is based on estimates of the parameters of the representation of a VAR model as a, possibly, two-sided infinite distributed lag model, we first show that a modification of Hannan's (1963, 1967) estimator is root-T consistent and asymptotically normal for the coefficients of such a representation. When the data is long-range dependent this method of estimation becomes more attractive than Least Squares, since the latter can be neither root-T consistent nor asymptotically normal as is the case with short-range dependent data.

Suggested Citation

  • Javier Hidalgo, 2000. "Nonparametric Test for Causality with Long-Range Dependence - (Now published in Econometrica, 68, (2000) pp.1465-1490," STICERD - Econometrics Paper Series 387, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:387
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    File URL: https://sticerd.lse.ac.uk/dps/em/em387.pdf
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    Cited by:

    1. Javier Hidalgo, 2007. "Specification Testing Forregression Models Withdependent Data," STICERD - Econometrics Paper Series 518, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

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