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Statistical inference on regression with spatial dependence

Author

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  • Peter Robinson

    (Institute for Fiscal Studies and London School of Economics)

  • Supachoke Thawornkaiwong

    (Institute for Fiscal Studies)

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.

Suggested Citation

  • Peter Robinson & Supachoke Thawornkaiwong, 2011. "Statistical inference on regression with spatial dependence," CeMMAP working papers CWP08/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:08/11
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    File URL: http://cemmap.ifs.org.uk/wps/cwp0811.pdf
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    Cited by:

    1. Javier Hidalgo & Myung Hwan Seo, 2013. "Specification For Lattice Processes," STICERD - Econometrics Paper Series 562, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Soutir Bandyopadhyay & Arnab Maity, 2018. "Asymptotic theory for varying coefficient regression models with dependent data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 745-759, August.

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