On Spatial Processes and Asymptotic Inference under Near-Epoch Dependence.
On Spatial Processes and Asymptotic Inference under Near-Epoch Dependence.
复制标题
DOI：
10.1016/j.jeconom.2012.05.022
复制
发表时间：
2012-09
影响因子：
6.3
通讯作者：
Prucha IR
中科院分区：
文献类型：
--
作者：
Jenish N;Prucha IR
由于缺乏适当的极限定理，非线性模型的一般推理理论的发展受到阻碍。为了便于一般的渐近推理理论相关的经济应用，本文首先扩展的概念，近历元依赖（NED）过程中使用的时间序列文献随机场。这类过程是NED的，比如说，一个α-混合过程，被证明是封闭的无限变换下，从而容纳模型与空间动力学。对于较小类的α混合过程，通常不是这种情况。本文进而导出了NED随机场的中心极限定理和大数定律。这些极限定理允许相当一般形式的异质性，包括渐近无界的时刻，并容纳阵列的随机场的不均匀间隔的格子。利用极限定理证明了GMM估计的相合性和渐近正态性。这些结果提供了一个基础，在广泛的空间依赖模型的推断。
The development of a general inferential theory for nonlinear models with cross-sectionally or spatially dependent data has been hampered by a lack of appropriate limit theorems. To facilitate a general asymptotic inference theory relevant to economic applications, this paper first extends the notion of near-epoch dependent (NED) processes used in the time series literature to random fields. The class of processes that is NED on, say, an α-mixing process, is shown to be closed under infinite transformations, and thus accommodates models with spatial dynamics. This would generally not be the case for the smaller class of α-mixing processes. The paper then derives a central limit theorem and law of large numbers for NED random fields. These limit theorems allow for fairly general forms of heterogeneity including asymptotically unbounded moments, and accommodate arrays of random fields on unevenly spaced lattices. The limit theorems are employed to establish consistency and asymptotic normality of GMM estimators. These results provide a basis for inference in a wide range of models with spatial dependence.
登录
查看更多内容
影响因子：
6.3
作者：
Conley, TG
通讯作者：
Conley, TG
影响因子：
0.8
作者：
BRADLEY, RC
通讯作者：
BRADLEY, RC
影响因子：
0.8
作者：
DAVIDSON, J
通讯作者：
DAVIDSON, J
DOI：
10.1137/1113026
发表时间：
1968-01-01
期刊：
THEORY OF PROBILITY AND ITS APPLICATIONS,USSR
影响因子：
--
作者：
DOBRUSCH.PL
通讯作者：
DOBRUSCH.PL
影响因子：
0.8
作者：
ANDREWS, DWK
通讯作者：
ANDREWS, DWK