Nonparametric Test for Causality with Long-Range Dependence - (Now published in Econometrica, 68, (2000) pp.1465-1490
AbstractThis 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.
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Bibliographic InfoPaper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2000/387.
Date of creation: Apr 2000
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Causality; long-range dependence; spectral analysis; distributed lag model; consistent test;
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Cowles Foundation Discussion Papers
977, Cowles Foundation for Research in Economics, Yale University.
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