GMM Estimation of Spatial Autoregressive Models with Moving Average Disturbances
GMM Estimation of Spatial Autoregressive Models with Moving Average Disturbances
复制标题
具有移动平均扰动的空间自回归模型的 GMM 估计
DOI：
10.2139/ssrn.2257110
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Suleyman Taspinar
中科院分区：
文献类型：
--
作者：
Suleyman Taspinar
在本文中，我们将Lee（2007 a）和Liu，Lee和Bollinger（2010）中考虑的一步广义矩量法（GMM）估计方法引入到对干扰项施加空间移动平均过程的空间模型中。首先，我们确定一组最佳的线性和二次矩函数的GMM估计。其次，我们表明，最佳的GMM估计（GMME）制定从这个集合是最有效的估计类GMME制定的线性和二次矩函数。我们的分析结果表明，一步GMME可以更有效的比准最大似然（QMLE），当干扰项是简单的i.i.d.通过广泛的Monte Carlo研究，我们将其有限样本性质与MLE，QMLE和Fingleton（2008 a）提出的估计进行了比较。
In this paper, we introduce the one-step generalized method of moments (GMM) estimation methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to spatial models that impose a spatial moving average process for the disturbance term. First, we determine the set of best linear and quadratic moment functions for GMM estimation. Second, we show that the optimal GMM estimator (GMME) formulated from this set is the most efficient estimator within the class of GMMEs formulated from the set of linear and quadratic moment functions. Our analytical results show that the one-step GMME can be more efficient than the quasi maximum likelihood (QMLE), when the disturbance term is simply i.i.d. With an extensive Monte Carlo study, we compare its finite sample properties against the MLE, the QMLE and the estimators suggested in Fingleton (2008a).
影响因子：
1.9
作者：
Kelejian, HH;Prucha, IR
通讯作者：
Prucha, IR