Efficient GMM estimation of spatial dynamic panel data models with fixed effects
Efficient GMM estimation of spatial dynamic panel data models with fixed effects
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DOI：
10.1016/j.jeconom.2014.03.003
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发表时间：
2014-06-01
影响因子：
6.3
通讯作者：
Yu, Jihai
中科院分区：
文献类型：
--
作者：
Lee, Lung-fei;Yu, Jihai
本文给出了具有固定效应的空间动态面板数据模型的GMM估计量的渐近性质，当n较大时，T可以较大，但相对于n较小.GMM估计方法的设计消除了模型中固定的个体和时间效应，即使在ML方法不可行或计算复杂的情况下，GMM估计方法也是计算容易的.如果在消除时间影响的同时不对空间权重矩阵进行行归一化，则最大似然估计方法将是不可行的，如果模型中有多个空间权重矩阵，则最大似然估计方法将在计算上困难；此外，如果与其他感兴趣的参数联合估计固定影响，则最大似然估计的一致性将要求T相对于n是大的而不是小的。GMM方法可以克服所有这些困难。我们使用外生变量和预定变量作为线性矩的工具，以及几个级别的相邻变量和附加的二次矩。我们将数据进行叠加，构造出最佳的线性和二次矩条件。另一种方法是对每个周期使用单独的矩条件，这会产生许多矩估计。我们证明了这些GMM估计是根NT相合的，渐近正态的，并且可以是相对有效的。我们用蒙特卡罗方法比较了这两种方法的有限样本性能。(C)2014爱思唯尔B.V.保留所有权利。
In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with fixed effects when n is large, and T can be large, but small relative to n. The GMM estimation methods are designed with the fixed individual and time effects eliminated from the model, and are computationally tractable even under circumstances where the ML approach would be either infeasible or computationally complicated. The ML approach would be infeasible if the spatial weights matrix is not row-normalized while the time effects are eliminated, and would be computationally intractable if there are multiple spatial weights matrices in the model; also, consistency of the MLE would require T to be large and not small relative to n if the fixed effects are jointly estimated with other parameters of interest. The GMM approach can overcome all these difficulties. We use exogenous and predetermined variables as instruments for linear moments, along with several levels of their neighboring variables and additional quadratic moments. We stack up the data and construct the best linear and quadratic moment conditions. An alternative approach is to use separate moment conditions for each period, which gives rise to many moments estimation. We show that these GMM estimators are root nT consistent, asymptotically normal, and can be relatively efficient. We compare these approaches on their finite sample performance by Monte Carlo. (C) 2014 Elsevier B.V. All rights reserved.