A nonparametric bootstrap test for nonlinear Granger causality

Cees Diks ()

No 3A.1, CeNDEF Workshop Papers, January 2001 from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance

Abstract: An information theoretic test for Granger causality for stationary weakly dependent time series is proposed. The test statistic is based on conditional entropies. These quantities have nonparametric estimators which can be expressed in terms of correlation integrals. This provides a connection with the method proposed by Baek and Brock (1992), and its modified version developed by Hiemstra and Jones (1994). Several methodological issues are discussed, and some changes to the traditional approach proposed. The significance of the test statistic is determined using the stationary bootstrap by Politis and Romano (1994), rather than using an asymptotic normal approximation. The size and power of the bootstrap test are determined for various econometric time series models by Monte Carlo simulations. It is argued that bootstrapping only the hypothesized non-causing time series is to be preferred over bootstrapping both time series. It is shown that in the latter case the test becomes equivalent to a bootstrap version of the test for Granger causality developed by Hiemstra and Jones (1994), with the additional advantage that a simplified test statistic can be used.

Date: 2001-01-04
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ams:cdws01:3a.1

Access Statistics for this paper

More papers in CeNDEF Workshop Papers, January 2001 from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands. Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().