Publication:
Inference on trending panel data

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorRobinson, Peter M.
dc.contributor.authorVelasco, Carlos
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2022-03-30T16:22:14Z
dc.date.available2022-03-30T16:22:14Z
dc.date.issued2018-10-01
dc.description.abstractSemiparametric panel data modelling and statistical inference with fractional stochastic trends, nonparametrically time-trending individual effects, and general cross-sectional correlation and heteroscedasticity in innovations are developed. The fractional stochastic trends allow for a wide range of nonstationarity, indexed by a memory parameter, nesting the familiar case and allowing for parametric short-memory. The individual effects can nonparametrically vary simultaneously across time and across units. The cross-sectional covariance matrix is also nonparametric. The main focus is on estimation of the time series parameters. Two methods are considered, both of which entail an only approximate differencing out of the individual effects, leaving an error which has to be taken account of in our theory. In both cases we obtain standard asymptotics, with a central limit theorem, over a wide range of possible parameter values, unlike the nonstandard asymptotics for autoregressive parameter estimates at a unit root. For statistical inference, consistent estimation of the limiting covariance matrix of the parameter estimates requires consistent estimation of a functional of the cross-sectional covariance matrix. We examine efficiency loss due to cross-sectional correlation in a spatial model example. A Monte Carlo study of finite-sample performance is included.en
dc.description.sponsorshipWe are grateful for the comments of two referees. Financial support from the Ministerio de Economía y Competitividad (Spain), grants ECO2012-31748, ECO2014-57007p and MDM 2014-0431, and Comunidad de Madrid, MadEco-CM (S2015/HUM-3444) is gratefully acknowledged by the second author.en
dc.identifier.bibliographicCitationRobinson, P. M., & Velasco, C. (2018). Inference on trending panel data. En Journal of Econometrics, 206 (2), pp. 282-304.es
dc.identifier.doihttps://doi.org/10.1016/j.jeconom.2018.06.003
dc.identifier.issn0304-4076
dc.identifier.publicationfirstpage282es
dc.identifier.publicationissue2es
dc.identifier.publicationlastpage304es
dc.identifier.publicationtitleJOURNAL OF ECONOMETRICSes
dc.identifier.publicationvolume206es
dc.identifier.urihttps://hdl.handle.net/10016/34498
dc.identifier.uxxiAR/0000029298
dc.language.isoenges
dc.publisherElsevieres
dc.relation.projectIDGobierno de España. ECO2012-31748es
dc.relation.projectIDComunidad de Madrid. S2015/HUM-3444es
dc.relation.projectIDGobierno de España. ECO2014-57007p
dc.rights© Elsevier, 2018es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEconomíaes
dc.subject.jelC12
dc.subject.jelC13
dc.subject.jelC23
dc.subject.otherSemiparametric panel data modellingen
dc.subject.otherNonparametrically time-trending individual effectsen
dc.subject.otherNonparametric cross-sectional correlation and heteroscedasticityen
dc.subject.otherSpatial modelen
dc.subject.otherParametric fractional dependenceen
dc.subject.otherConsistencyen
dc.subject.otherAsymptotic normalityen
dc.titleInference on trending panel dataen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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