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dc.contributor.authorRobinson, Peter M.en
dc.date.accessioned2011-03-25-
dc.date.accessioned2012-10-16T13:11:09Z-
dc.date.available2012-10-16T13:11:09Z-
dc.date.issued2011-
dc.identifier.pidoi:10.1920/wp.cem.2011.1111en
dc.identifier.urihttp://hdl.handle.net/10419/64675-
dc.description.abstractNonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects spatial correlation. Conditional heteroscedasticity is also allowed, as well as non-identically distributed observations. Instead of mixing conditions, a (possibly non-stationary) linear process is assumed for disturbances, allowing for long range, as well as short-range, dependence, while decay in dependence in explanatory variables is described using a measure based on the departure of the joint density from the product of marginal densities. A basic triangular array setting is employed, with the aim of covering various patterns of spatial observation. Sufficient conditions are established for consistency and asymptotic normality of kernel regression estimates. When the cross-sectional dependence is sufficiently mild, the asymptotic variance in the central limit theorem is the same as when observations are independent; otherwise, the rate of convergence is slower. We discuss application of our conditions to spatial autoregressive models, and models defined on a regular lattice.en
dc.language.isoengen
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen
dc.relation.ispartofseries|acemmap working paper |xCWP11/11en
dc.subject.jelC13en
dc.subject.jelC14en
dc.subject.jelC21en
dc.subject.ddc330en
dc.subject.keywordNonparametric regressionen
dc.subject.keywordSpatial dataen
dc.subject.keywordWeak dependenceen
dc.subject.keywordLong range dependenceen
dc.subject.keywordHeterogeneityen
dc.subject.keywordConsistencyen
dc.subject.keywordCentral limit theoremen
dc.titleAsymptotic theory for nonparametric regression with spatial data-
dc.typeWorking Paperen
dc.identifier.ppn654842795en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:ifs:cemmap:11/11en

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