Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models
Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models
复制标题
空间自回归模型的异方差一致协方差矩阵估计器
DOI：
10.1080/17421772.2019.1549366
复制
发表时间：
2019
影响因子：
2.3
通讯作者：
Anil K. Bera
中科院分区：
文献类型：
--
作者：
Suleyman Taspinar;Osman Doğan;Anil K. Bera
摘要在存在异方差的情况下，传统的基于普通最小二乘（OLS）估计量的检验统计量会导致线性回归模型的推断结果不正确。鉴于异方差性在横截面数据中很常见，基于各种形式的异方差一致性协方差矩阵（HCCMs）的检验统计量已在文献中得到发展。与标准线性回归模型相比，异方差是空间计量经济模型的一个更严重的问题，通常会导致模型系数极值估计不一致。本文研究了具有未知异方差形式的空间计量经济模型的异方差-鲁棒广义矩估计方法的有限样本性质。特别是，它开发了各种hccm型校正，以改善RGMME和常规Wald测试的有限样本性能。蒙特卡罗结果表明，在小样本情况下，hccm型修正对模型参数的推断和冲击效应的估计结果更为准确。
ABSTRACT In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.
影响因子：
1.9
作者：
Kelejian, HH;Prucha, IR
通讯作者：
Prucha, IR