Interpolation, outliers and inverse autocorrelations

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dc.contributor.author Peña, Daniel
dc.contributor.author Maravall, Agustín
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned 2008-07-22T10:12:20Z
dc.date.available 2008-07-22T10:12:20Z
dc.date.issued 1990-02
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/2770
dc.description.abstract The paper addresses the problem of estimating missing observations in linear, possibly nonstationary, stochastic processes when the model is known. The general case of any possible distribution of missing observations in the time series is considered, and analytical expressions for the optimal estimators and their associated mean squared errors are obtained. These expressions involve solely the elements of the inverse or dual autocorrelation function of the series. This optimal estimator -the conditional expectation of the missing observations given the available ones-is equal oto the estimator that results from filling the missing values in the series with arbitrary numbers, treating these numbers as additive outliers, and removing the outlier effects from the invented numbers using intervention analysis.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries Working Papers
dc.relation.ispartofseries 1991-08
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Missing observations
dc.subject.other Outliers
dc.subject.other Intervention analysis
dc.subject.other ARIMA models
dc.subject.other Inverse autocorrelation function
dc.title Interpolation, outliers and inverse autocorrelations
dc.type workingPaper
dc.type workingPaper
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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