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Statistical inference on regression with spatial dependence

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  • Peter Robinson

    ()
    (Institute for Fiscal Studies and London School of Economics)

  • Supachoke Thawornkaiwong
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    Abstract

    Central limit theorems are developed for instrumental variables estimates of linear and semiparametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss estimation of the variance matrix, including estimates that are robust to disturbance heteroscedasticity and/or dependence. A Monte Carlo study of finite-sample performance is included. In an empirical example, the estimates and robust and non-robust standard errors are computed from Indian regional data, following tests for spatial correlation in disturbances, and nonparametric regression fitting. Some final comments discuss modifications and extensions.

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    File URL: http://cemmap.ifs.org.uk/wps/cwp0811.pdf
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    Bibliographic Info

    Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP08/11.

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    Date of creation: Feb 2011
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    Handle: RePEc:ifs:cemmap:08/11

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