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Testing for random effects and serial correlation in spatial autoregressive models

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Title: Testing for random effects and serial correlation in spatial autoregressive models
Authors: Montes-Rojas, Gabriel V.1 Gabriel.Montes-Rojas.1@city.ac.uk
Source: Journal of Statistical Planning & Inference. Apr2010, Vol. 140 Issue 4, p1013-1020. 8p.
Subjects: Statistical correlation, Autoregression (Statistics), Mathematical models, Estimation theory, Maximum likelihood statistics, Instrumental variables (Statistics), Monte Carlo method
Abstract: Abstract: This paper constructs and evaluates tests for random effects and serial correlation in spatial autoregressive panel data models. In these models, ignoring the presence of random effects not only produces misleading inference but inconsistent estimation of the regression coefficients. Two different estimation methods are considered: maximum likelihood and instrumental variables. For each estimator, optimal tests are constructed: Lagrange multiplier in the first case; Neyman''s in the second. In addition, locally size-robust tests, for individual hypotheses under local misspecification of the unconsidered parameter, are constructed. Extensive Monte Carlo evidence is presented. [Copyright &y& Elsevier]
Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 47113653
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Testing for random effects and serial correlation in spatial autoregressive models
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  Data: <searchLink fieldCode="AR" term="%22Montes-Rojas%2C+Gabriel+V%2E%22">Montes-Rojas, Gabriel V.</searchLink><relatesTo>1</relatesTo><i> Gabriel.Montes-Rojas.1@city.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Statistical+Planning+%26+Inference%22">Journal of Statistical Planning & Inference</searchLink>. Apr2010, Vol. 140 Issue 4, p1013-1020. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Autoregression+%28Statistics%29%22">Autoregression (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Instrumental+variables+%28Statistics%29%22">Instrumental variables (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink>
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  Data: Abstract: This paper constructs and evaluates tests for random effects and serial correlation in spatial autoregressive panel data models. In these models, ignoring the presence of random effects not only produces misleading inference but inconsistent estimation of the regression coefficients. Two different estimation methods are considered: maximum likelihood and instrumental variables. For each estimator, optimal tests are constructed: Lagrange multiplier in the first case; Neyman''s in the second. In addition, locally size-robust tests, for individual hypotheses under local misspecification of the unconsidered parameter, are constructed. Extensive Monte Carlo evidence is presented. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.jspi.2009.10.001
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        Text: English
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        PageCount: 8
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    Subjects:
      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: Autoregression (Statistics)
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Estimation theory
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      – SubjectFull: Maximum likelihood statistics
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      – SubjectFull: Instrumental variables (Statistics)
        Type: general
      – SubjectFull: Monte Carlo method
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      – TitleFull: Testing for random effects and serial correlation in spatial autoregressive models
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              Text: Apr2010
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