Robinson, Peter M. and Thawornkaiwong, Supachoke (2012) Statistical inference on regression with spatial dependence. Journal of Econometrics, 167 (2). pp. 521-542. ISSN 0304-4076
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Official URL: | http://www.journals.elsevier.com/journal-of-econom... |
| Additional Information: | © 2012 Elsevier |
| Divisions: | Economics |
| Subjects: | H Social Sciences > HB Economic Theory |
| JEL classification: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models |
| Date Deposited: | 23 Nov 2011 13:54 |
| Last Modified: | 06 Nov 2024 08:03 |
| URI: | http://eprints.lse.ac.uk/id/eprint/39713 |
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