Non-nested testing of spatial correlation
Non-nested testing of spatial correlation
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DOI：
10.1016/j.jeconom.2015.02.044
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发表时间：
2013-11
影响因子：
6.3
通讯作者：
Miguel A. Delgado;P. Robinson
中科院分区：
文献类型：
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作者：
Miguel A. Delgado;P. Robinson
我们在一般的空间，时空或面板数据背景下开发非嵌套测试。空间方面可以相当普遍地解释，无论是在地理意义上，或采用经济距离的概念，或当参数建模部分来自一个共同的因素或其他结构。在前一种情况下，观测可以在一个或多个维度上规则地间隔开，这对于许多时空数据是典型的，或者在所有维度上不规则地间隔开;可以考虑各向同性模型和非各向同性模型，以及各种各样的相关结构。在第二种情况下，涉及空间权重矩阵的模型，如“空间自回归模型”。设置是足够普遍的潜在覆盖其他参数结构，如某些因子模型，和向量值的观察，在这里我们的初步渐近理论参数估计是一些独立的价值。检验统计量是基于高斯伪似然比，并显示有一个渐进的标准正态分布下的零假设，这两个模型之一是正确的，这个极限理论依赖于强中央极限定理的高斯伪最大似然参数估计。一个小的Monte Carlo研究有限样本的性能。
We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, or when parametric modelling arises in part from a common factor or other structure. In the former case, observations may be regularly-spaced across one or more dimensions, as is typical with much spatio-temporal data, or irregularly-spaced across all dimensions; both isotropic models and non-isotropic models can be considered, and a wide variety of correlation structures. In the second case, models involving spatial weight matrices are covered, such as “spatial autoregressive models”. The setting is sufficiently general to potentially cover other parametric structures such as certain factor models, and vector-valued observations, and here our preliminary asymptotic theory for parameter estimates is of some independent value. The test statistic is based on a Gaussian pseudo-likelihood ratio, and is shown to have an asymptotic standard normal distribution under the null hypothesis that one of the two models is correct; this limit theory rests strongly on a central limit theorem for the Gaussian pseudo-maximum likelihood parameter estimates. A small Monte Carlo study of finite-sample performance is included.