Efficient estimation of the semiparametric spatial autoregressive model
Efficient estimation of the semiparametric spatial autoregressive model
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DOI：
10.1016/j.jeconom.2009.10.031
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发表时间：
2006-05
影响因子：
6.3
通讯作者：
P. Robinson
中科院分区：
文献类型：
--
作者：
P. Robinson
有效的半参数和参数估计空间自回归模型，包含非随机解释变量和创新怀疑是非正态的。主要的压力是未知的，非参数的，形式的分布的情况下，一系列的非参数估计的得分函数采用自适应估计的参数的兴趣。这些估计与基于正确形式的估计一样有效，特别是在非高斯分布下，它们比伪高斯最大似然估计更有效。两种不同的自适应估计被认为是依赖于不同的规律性条件。包括有限样本性能的蒙特卡罗研究。
Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing non-stochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as the ones based on a correct form, in particular they are more efficient than pseudo-Gaussian maximum likelihood estimates at non-Gaussian distributions. Two different adaptive estimates are considered, relying on somewhat different regularity conditions. A Monte Carlo study of finite sample performance is included.