Inference on higher-order spatial autoregressive models with increasingly many parameters
Inference on higher-order spatial autoregressive models with increasingly many parameters
复制标题
DOI：
10.1016/j.jeconom.2014.12.008
复制
发表时间：
2015-05-01
影响因子：
6.3
通讯作者：
Robinson, Peter M.
中科院分区：
文献类型：
--
作者：
Gupta, Abhimanyu;Robinson, Peter M.
本文研究了高阶空间自回归模型参数估计的相合性和渐近正态性，该模型的阶数和回归变量的个数随样本容量的变化缓慢地趋于无穷大.最小二乘和工具变量估计进行检查，并允许增长率的参数空间的尺寸相对于样本大小进行了研究。除了允许参数的数量随着数据的增加，这具有适应某些空间设置，其中几个讨论所建议的一些渐近制度的优点。还包括一个小的实证例子，和蒙特卡罗研究分析有限样本的理论的各种影响。(C)2015作者由爱思唯尔公司出版
This paper develops consistency and asymptotic normality of parameter estimates for a higher-order spatial autoregressive model whose order, and number of regressors, are allowed to approach infinity slowly with sample size. Both least squares and instrumental variables estimates are examined, and the permissible rate of growth of the dimension of the parameter space relative to sample size is studied. Besides allowing the number of parameters to increase with the data, this has the advantage of accommodating some asymptotic regimes that are suggested by certain spatial settings, several of which are discussed. A small empirical example is also included, and a Monte Carlo study analyses various implications of the theory in finite samples. (C) 2015 The Authors. Published by Elsevier B.V.