Missing observations in ARIMA models: skipping strategy versus additive outlier approach

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dc.contributor.author Gómez, Víctor
dc.contributor.author Maravall, Agustín
dc.contributor.author Peña, Daniel
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2011-03-23T19:03:35Z
dc.date.available 2011-03-23T19:03:35Z
dc.date.issued 1997-02
dc.identifier.uri http://hdl.handle.net/10016/10576
dc.description.abstract Optimal estimation of missing values in ARMA models is typically performed by using the Kalman Filter for likelihood evaluation, "skipping" in the computations the missing observations, obtaining the maximum likelihood (ML) estimators of the model parameters, and using some smoothing algorithm. The same type of procedure has been extended to nonstationary ARIMA models in G6mez Maravall (1994). An alternative procedure suggests filling in the holes in the series with arbitrary values and then performing ML estimation of the ARIMA model with Additive Outliers (AO). When the model parameters are not known the two methods differ, since the AO likelihood is affected by the arbitrary values. We develop the proper likelihood for the AO approach in the general non-stationary case and show the equivalence of this and the skipping method. Computationally efficient ways to apply both procedures, based on an Augmented Kalman Filter, are detailed. Finally, the two methods are compared through simulation, and their relative advantages assessed; the comparison also includes the AO method with the uncorrected likelihood.
dc.format.mimetype application/octet-stream
dc.format.mimetype application/octet-stream
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 97-15
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Time series
dc.subject.other ARIMA models
dc.subject.other Missing observations
dc.subject.other Outliers
dc.subject.other Nonstationarity
dc.subject.other Likelihood
dc.subject.other Kalman filter
dc.title Missing observations in ARIMA models: skipping strategy versus additive outlier approach
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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