Statistical inference on regression with spatial dependence |
| |
Authors: | Peter M Robinson Supachoke Thawornkaiwong |
| |
Institution: | London School of Economics, United Kingdom |
| |
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. |
| |
Keywords: | C13 C14 C21 |
本文献已被 ScienceDirect 等数据库收录! |
|