打印

作者

来源

REGIONAL SCIENCE AND URBAN ECONOMICS,Vol.72,IssueSI,P.35-57

语言

英文

关键字

LM test; OPG; C(alpha) test; GMM test; Unknown heteroskedasticity; Spatial dependence

作者单位

[Jin, Fei] Shanghai Univ Finance & Econ, Sch Econ, Shanghai 200433, Peoples R China. [Jin, Fei] Minist Educ, Key Lab Math Econ SUFE, Shanghai 200433, Peoples R China. [Lee, Lung-fei] Ohio State Univ, Dept Econ, Columbus, OH 43210 USA. Jin, F (reprint author), Shanghai Univ Finance & Econ, Sch Econ, Shanghai 200433, Peoples R China. E-Mail: jin.fei@sufe.edu.cn; lee.1777@osu.edu

摘要

For Lagrangian multiplier (LM) tests of restrictions on parameters in spatial autoregressive (SAR) models with (SARAR models) or without SAR disturbances, their outer-product-of-gradient (OPG) variants can be simple and robust to unknown heteroskedasticity. However, for certain tests, asymptotic distributions of test statistics might depend on the constrained maximum likelihood or quasi maximum likelihood (QML) estimators, so their OPG variants would not be valid. To overcome such a hurdle, we propose to use C(alpha)-type score vectors to obtain valid OPG variants. Such OPG tests can be systematically constructed for SARAR models with homoskedastic and heteroskedastic disturbances, which might not be normally distributed. They also have the advantage that any root n - consistent estimator can be used in place of a restricted QML estimate. In particular, OPG tests based on generalized method of moments (GMM) estimates are computationally simple and powerful compared to LM tests. Corresponding OPG tests based on C(alpha)-type gradient vectors in the GMM framework are also investigated.