Bayesian Methods for Completing Data in Spatial Models

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dc.contributor.author Llano, Carlos
dc.contributor.author Polasek, Wolfgang
dc.contributor.author Sellner, Richard
dc.contributor.other UAM. Departamento de Análisis Económico, Teoría Económica e Historia Económica es_ES
dc.date.accessioned 2015-09-07T16:15:07Z
dc.date.available 2015-09-07T16:15:07Z
dc.date.issued 2010
dc.identifier.citation Review of Economic Analysis 2 (2010): 194–214 en_US
dc.identifier.issn 1973-3909
dc.identifier.uri http://hdl.handle.net/10486/667825 en
dc.description.abstract Completing data sets that are collected in heterogeneous units is a quite frequent problem. Chow and Lin (1971) were the first to develop a unified framework for the three problems (interpolation, extrapolation and distribution) of predicting times series by related series (the ‘indicators’). This paper develops a spatial Chow-Lin procedure for cross-sectional data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for ML and Bayesian MCMC estimation. In an example, we apply the procedure to Spanish regional GDP data between 2000 and 2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3 level by using socio-economic and spatial information available at NUTS-3. The spatial neighborhood is defined by either km distance, travel time, contiguity or trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted values with the observed ones. en_US
dc.description.sponsorship This paper is part of a project funded by the Jubilaeumsfonds of the Austrian National Bank (OeNB). en_US
dc.format.extent 21 p.
dc.format.mimetype application/pdf en
dc.language.iso eng
dc.publisher Rimini Centre for Economic Analysis en_US
dc.relation.ispartof Review of Economic Analysis en_US
dc.subject.other Interpolation en_US
dc.subject.other Spatial econometrics en_US
dc.subject.other MCMC en_US
dc.subject.other Spatial Chow-Lin en_US
dc.subject.other Missing regional data en_US
dc.subject.other Spatial autoregression en_US
dc.subject.other Forecasting by MCMC en_US
dc.subject.other NUTS en_US
dc.title Bayesian Methods for Completing Data in Spatial Models en_US
dc.type article en_US
dc.subject.eciencia Estadística y Demografía / Estadística es_ES
dc.identifier.publicationfirstpage 194
dc.identifier.publicationissue 2
dc.identifier.publicationlastpage 214
dc.type.version info:eu-repo/semantics/publishedVersion en
dc.rights.cc Reconocimiento – NoComercial es_ES
dc.rights.accessRights openAccess en_US
dc.authorUAM Llano Verduras, Carlos (261385)


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