Semiparametric Estimation of Long-Memory Models

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dc.contributor.author Velasco, Carlos
dc.date.accessioned 2009-06-29T14:31:39Z
dc.date.accessioned 2010-11-10T13:25:15Z
dc.date.available 2010-11-10T13:25:15Z
dc.date.issued 2006
dc.identifier.bibliographicCitation Patterson, K. ; Mills, T.C. (eds.). Palgrave Handbook of Econometrics: Econometric Theory. New York: Palgrave, MacMillan, 2006, vol. I, p. 353-395
dc.identifier.isbn 1403941556
dc.identifier.uri http://hdl.handle.net/10016/4530
dc.description.abstract This chapter reviews semiparametric methods of inference on different aspects of long memory time series. The main focus is on estimation of the memory parameter of linear models, analyzing bandwidth choice, bias reduction techniques and robustness properties of different estimates, with sorne emphasis on nonstationarity and trending behaviors. These techniques extend naturally to multivariate series, where the important issues are the estimation of the long-run relationship and testing for fractional cointegration. Specific techniques for the estimation of the degree of persistence of volatility for nonlinear time series are also considered.
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso eng
dc.publisher Palgrave Macmillan
dc.title Semiparametric Estimation of Long-Memory Models
dc.type bookPart
dc.type.review PeerReviewed
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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