Statistical inference on regression with spatial dependence

被引:13
作者
Robinson, Peter M. [1 ]
Thawornkaiwong, Supachoke [1 ]
机构
[1] London Sch Econ, London, England
关键词
Linear regression; Partly linear regression; Nonparametric regression; Spatial data; Instrumental variables; Asymptotic normality; Variance estimation; TIME-SERIES REGRESSION; SEMIPARAMETRIC REGRESSION; ADAPTIVE ESTIMATION; RANDOM-FIELDS; MODELS;
D O I
10.1016/j.jeconom.2011.09.033
中图分类号
F [经济];
学科分类号
02 ;
摘要
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.
引用
收藏
页码:521 / 542
页数:22
相关论文
共 24 条
[1]  
[Anonymous], 1973, Advances in Applied Probability, DOI DOI 10.2307/1425967
[2]   History, institutions, and economic performance: The legacy of colonial land tenure systems in India [J].
Banerjee, A ;
Iyer, L .
AMERICAN ECONOMIC REVIEW, 2005, 95 (04) :1190-1213
[3]   ON THE CENTRAL LIMIT-THEOREM FOR STATIONARY MIXING RANDOM-FIELDS [J].
BOLTHAUSEN, E .
ANNALS OF PROBABILITY, 1982, 10 (04) :1047-1050
[4]   ON SMOOTHED PROBABILITY DENSITY-ESTIMATION FOR STATIONARY-PROCESSES [J].
CASTELLANA, JV ;
LEADBETTER, MR .
STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 1986, 21 (02) :179-193
[5]   EFFICIENCY BOUNDS FOR SEMIPARAMETRIC REGRESSION [J].
CHAMBERLAIN, G .
ECONOMETRICA, 1992, 60 (03) :567-596
[6]   GMM estimation with cross sectional dependence [J].
Conley, TG .
JOURNAL OF ECONOMETRICS, 1999, 92 (01) :1-45
[7]  
Eicker F., 1967, 5 BERK SUMP MATH STA, P59
[8]   Root-n-consistent estimation of partially linear time series models [J].
Fan, YQ ;
Li, Q .
JOURNAL OF NONPARAMETRIC STATISTICS, 1999, 11 (1-3) :251-269
[9]   Adaptive estimation in partially linear autoregressive models [J].
Gao, JT ;
Yee, T .
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2000, 28 (03) :571-586
[10]   Semiparametric regression smoothing of non-linear time series [J].
Gao, JT .
SCANDINAVIAN JOURNAL OF STATISTICS, 1998, 25 (03) :521-539