.png){kind=icon}
Return
Statistical inference on regression with spatial dependence
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2011.09.033.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Linear regression
Partly linear regression
Nonparametric regression
Spatial data
Instrumental variables
Asymptotic normality
Variance estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.3K
Citations:
3.0W
Organization
Cited Papers
.png){kind=icon}
Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution
PREAI
ShareSaveShareSaveShareSaveShareSave
View more