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A Score Test for Individual Heteroscedasticity in a One-way Error Components Model

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Author Info
Alberto HOLLY
Lucien GARDIOL

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Abstract

The purpose of this paper is to derive a Rao's efficient score statistic for testing for heteroscedasticity in an error components model with only individual effects. We assume that the individual effect exists and therefore do not test for it. In addition, we assume that the individual effects, and not the white noise term may be heteroscedastic. Finally, we assume that the error components are normally distributed. We first establish, under a specific set of assumptions, the asymptotic distribution of the Score under contiguous alternatives. We then derive the expression for the Score test statistic for individual heteroscedasticity. Finally, we discuss the asymptotic local power of this Score test statistic.

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Publisher Info
Paper provided by Université de Lausanne, Faculté des HEC, DEEP in its series Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) with number 9915.

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Length: 18 pages
Date of creation: Sep 1999
Date of revision:
Handle: RePEc:lau:crdeep:9915

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Postal: Université de Lausanne, Faculté des HEC, DEEP, Internef, CH-1015 Lausanne
Phone: ++41 21 692.33.64
Fax: ++41 21 692.33.65
Web page: http://www.hec.unil.ch/deep/publications-english/e-cahiers.htm

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Related research
Keywords: panel data; error components model; score test; individual heteroscedasticity: contiguous alternatives; asymptotic local power;

Other versions of this item:

Find related papers by JEL classification:
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Magnus, Jan R., 1978. "Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix," Journal of Econometrics, Elsevier, vol. 7(3), pages 281-312, April. [Downloadable!] (restricted)
  2. Breusch, T S & Pagan, A R, 1980. "The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics," Review of Economic Studies, Blackwell Publishing, vol. 47(1), pages 239-53, January. [Downloadable!] (restricted)
  3. Gourieroux, Christian & Holly, Alberto & Monfort, Alain, 1981. "Kuhn-Tucker, likelihood ratio and Wald tests for nonlinear models with inequality constraints on the parameters," Journal of Econometrics, Elsevier, vol. 16(1), pages 166-166, May. [Downloadable!] (restricted)
  4. Gallant, A Ronald & Holly, Alberto, 1980. "Statistical Inference in an Implicit, Nonlinear, Simultaneous Equation Model in the Context of Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 48(3), pages 697-720, April. [Downloadable!] (restricted)
  5. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-94, September. [Downloadable!] (restricted)
  6. Baltagi, Badi H. & Chang, Young-Jae & Li, Qi, 1992. "Monte Carlo results on several new and existing tests for the error component model," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 95-120. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Byeong Park & Seuck Song, 2007. "Comments on: Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 16(1), pages 47-51, May. [Downloadable!] (restricted)
  2. Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2005. "Joint LM Test for Homoskedasticity in a One-Way error Component Model," Center for Policy Research Working Papers 72, Center for Policy Research, Maxwell School, Syracuse University. [Downloadable!]
    Other versions:
  3. Badi H. Baltagi & Byoung Cheol Jung & Seuck Heun Song, 2008. "Testing for Heteroskedasticity and Serial Correlation in a Random Effects Panel Data Model," Center for Policy Research Working Papers 111, Center for Policy Research, Maxwell School, Syracuse University. [Downloadable!]
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