Statistical inference on regression with spatial dependence

Peter Robinson () and Supachoke Thawornkaiwong
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Peter Robinson: Institute for Fiscal Studies and London School of Economics
Supachoke Thawornkaiwong: Institute for Fiscal Studies

No CWP08/11, CeMMAP working papers from Centre for Microdata Methods and Practice, 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.

Date: 2011-02-14
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
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