Arbeitspapier
Statistical inference on regression with spatial dependence
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.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP08/11
- Classification
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Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Subject
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Linear regression
Partly linear regression
Nonparametric regression
Spatial data
Instrumental variables
Asymptotic normality
Variance estimation
Regression
Nichtparametrisches Verfahren
Varianzanalyse
- Event
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Geistige Schöpfung
- (who)
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Robinson, Peter M.
Thawornkaiwong, Supachoke
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2011
- DOI
-
doi:10.1920/wp.cem.2011.0811
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Robinson, Peter M.
- Thawornkaiwong, Supachoke
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2011
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