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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number
/2000/387.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Toda, Hiro Y & Phillips, Peter C B, 1993.
"Vector Autoregressions and Causality,"
Econometrica,
Econometric Society, vol. 61(6), pages 1367-93, November.
[Downloadable!] (restricted)
Other versions: