Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64781
Authors: 
Robinson, Peter M.
Thawornkaiwong, Supachoke
Year of Publication: 
2011
Series/Report no.: 
cemmap working paper CWP08/11
Abstract: 
Central limit theorems are developed for instrumental variables estimates of linear and semi-parametric partly linear regression models for spatial data. General forms of spatial dependenceand 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.
Subjects: 
Linear regression
Partly linear regression
Nonparametric regression
Spatial data
Instrumental variables
Asymptotic normality
Variance estimation
JEL: 
C13
C14
C21
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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