Statistical inference on regression with spatial dependence

Peter M. Robinson and Supachoke Thawornkaiwong

Journal of Econometrics, 2012, vol. 167, issue 2, 521-542

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.

Keywords: Linear regression; Partly linear regression; Nonparametric regression; Spatial data; Instrumental variables; Asymptotic normality; Variance estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:167:y:2012:i:2:p:521-542

DOI: 10.1016/j.jeconom.2011.09.033

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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