Correlation testing in time series, spatial and cross-sectional data
Correlation testing in time series, spatial and cross-sectional data
复制标题
DOI：
10.1016/j.jeconom.2008.09.001
复制
发表时间：
2008-11-01
影响因子：
6.3
通讯作者：
Robinson, P. M.
中科院分区：
文献类型：
--
作者：
Robinson, P. M.
我们提供了一个通用类的时间序列，空间，时空和横截面数据的相关性测试。我们激励我们的重点是通过审查如何计算和理论上的困难点估计安装，从定期间隔的时间序列数据，通过形式的不规则的间距，和各种空间数据。一大类计算简单的测试是合理的。这些专门针对各种参数偏离的拉格朗日乘数检验。它们的形式示出的情况下，几个模型来描述各种数据的相关性。最初的重点假设同方差，但我们也鲁棒的非参数异方差的测试。(C)2008 Elsevier B.V.保留所有权利。
We provide a general class of tests for correlation in time series, spatial, spatio-temporal and crosssectional data. We motivate our focus by reviewing how Computational and theoretical difficulties of point estimation mount, as one moves from regularly-spaced time series data, through forms of irregular spacing, and to spatial data of various kinds. A broad class Of computationally simple tests is justified. These specialize to Lagrange multiplier tests against parametric departures of various kinds. Their forms are illustrated in case of several models for describing correlation in various kinds of data. The initial focus assumes homoscedasticity, but we also robustify the tests to nonparametric heteroscedasticity. (C) 2008 Elsevier B.V. All rights reserved.