Asymptotic inference results for multivariate long-memory processes

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dc.contributor.author Dolado, Juan José
dc.contributor.author Mármol, Francesc
dc.date.accessioned 2009-01-30T12:49:15Z
dc.date.available 2009-01-30T12:49:15Z
dc.date.issued 2004
dc.identifier.bibliographicCitation The Econometrics Journal, 2004, 7, 1, p. 168-190
dc.identifier.issn 1368-4221
dc.identifier.uri http://hdl.handle.net/10016/3207
dc.description.abstract In this paper, we extend the well-known Sims, Stock and Watson (SSW)(Sims et al. 1990; Econometrica 56, 113?44), analysis on estimation and testing in vector autoregressive process (VARs) with integer unit roots and deterministic components to a more general set-up where non-stationary fractionally integrated (NFI) processes are considered. In particular, we focus on partial VAR models where the conditioning variables are NFI since this is the only finite-lag VAR model compatible with such processes. We show how SSW?s conclusions remain valid. This means that whenever a block of coefficients in the partial VAR can be written as coefficients on zero-mean I(0) regressors in models including a constant term, they will have a joint asymptotic normal distribution. Monte Carlo simulations and an empirical application of our theoretical results are also provided.
dc.format.mimetype text/plain
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Blackwell
dc.rights © Blackwell
dc.subject.other Vector fractionally integrated processes
dc.subject.other Fractional cointegration
dc.subject.other Granger causality
dc.subject.other Permanent income hypothesis
dc.title Asymptotic inference results for multivariate long-memory processes
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion The definitive version is available at www.blackwell-synergy.com
dc.relation.publisherversion http://dx.doi.org/10.1111/j.1368-423X.2004.00126.x
dc.subject.eciencia Economía
dc.identifier.doi 10.1111/j.1368-423X.2004.00126.x
dc.rights.accessRights openAccess
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