Testing for serial correlation, spatial autocorrelation and random effects using panel data
Testing for serial correlation, spatial autocorrelation and random effects using panel data
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DOI：
10.1016/j.jeconom.2006.09.001
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发表时间：
2007-09-01
影响因子：
6.3
通讯作者：
Koh, Won
中科院分区：
文献类型：
--
作者：
Baltagi, Badi H.;Song, Seuck Heun;Koh, Won
本文考虑了一种空间面板数据回归模型，该模型具有各空间单元随时间的序列相关性以及各空间单元在各时间点的空间依赖性。此外，该模型允许使用随机效应的跨空间单元的异质性。然后，本文推导了该面板数据回归模型的拉格朗日乘数检验，包括序列相关、空间自相关和随机效应的联合检验。这些测试借鉴了两方面的早期工作。首先是在Anselin和Bera[1998]中讨论的空间误差相关模型的LM检验。线性回归模型的空间依赖性与空间计量经济学的介绍。见：Ullah， A., Giles， D.E.A.（编），《应用经济统计手册》。马塞尔•德克尔。Baltagi et al.[2003]在面板数据背景下。检验具有空间误差相关性的面板数据回归模型。经济研究[j].中国科学：自然科学版。二是对Baltagi和Li[1995]导出的具有序列相关的误差分量面板数据模型进行LM检验。在误差分量模型中测试AR(1)对MA(1)干扰的影响。经济研究[j]。因此，本文导出的联合LM测试包含了早期工作中两个分支中导出的测试。事实上，在我们一般模型的背景下，早期的LM测试变成了边际LM测试，忽略了随时间的序列相关性或空间误差相关性。然后，本文推导出不忽略这些相关性的条件LM和LR测试，并将它们与边际LM和LR对应的测试进行对比。利用蒙特卡罗实验对这些测试的小样本性能进行了研究。正如预期的那样，忽略任何重要的相关性会导致误导性的推断。(C) 2006 Elsevier B.V.版权所有
This paper considers a spatial panel data regression model with serial correlation on each spatial unit over time as well as spatial dependence between the spatial units at each point in time. In addition, the model allows for heterogeneity across the spatial units using random effects. The paper then derives several Lagrange multiplier tests for this panel data regression model including a joint test for serial correlation, spatial autocorrelation and random effects. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin and Bera [1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah, A., Giles, D.E.A. (Eds.), Handbook of Applied Economic Statistics. Marcel Dekker. New York] and in the panel data context by Baltagi et al. [2003. Testing panel data regression models with spatial error correlation. Journal of Econometrics 117, 123-150]. The second is the LM tests for the error component panel data model with serial correlation derived by Baltagi and Li [1995. Testing AR(1) against MA(1) disturbances in an error component model. Journal of Econometrics 68, 133-151]. Hence, the joint LM test derived in this paper encompasses those derived in both strands of earlier works. In fact, in the context of our general model, the earlier LM tests become marginal LM tests that ignore either serial correlation over time or spatial error correlation. The paper then derives conditional LM and LR tests that do not ignore these correlations and contrast them with their marginal LM and LR counterparts. The small sample performance of these tests is investigated using Monte Carlo experiments. As expected, ignoring any correlation when it is significant can lead to misleading inference. (C) 2006 Elsevier B.V. All rights reserved.