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Testing for Heteroskedasticity and Serial Correlation in a Random Effects Panel Data Model

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Abstract

This paper considers a panel data regression model with heteroskedastic as well as serially correlated disturbances, and derives a joint LM test for homoskedasticity and no first order serial correlation. The restricted model is the standard random individual error component model. It also derives a conditional LM test for homoskedasticity given serial correlation, as well as a conditional LM test for no first order serial correlation given heteroskedasticity, all in the context of a random effects panel data model. Monte Carlo results show that these tests, along with their likelihood ratio alternatives, have good size and power under various forms of heteroskedasticity including exponential and quadratic functional forms.

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Bibliographic Info

Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 111.

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Length: 53 pages
Date of creation: Dec 2008
Date of revision:
Handle: RePEc:max:cprwps:111

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Keywords: Panel data; heteroskedasticity; serial correlation; Lagrange Multiplier tests; likelihood ratio; random effects;

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References

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  1. Stengos, T. & Li, Q., 1993. "Adaptive Estimation in the Panel Data Error Component Model with Heteroskedasticity of Unknown Form," Working Papers 1993-4, University of Guelph, Department of Economics and Finance.
  2. Bera, Anil K. & Sosa-Escudero, Walter & Yoon, Mann, 2001. "Tests for the error component model in the presence of local misspecification," Journal of Econometrics, Elsevier, vol. 101(1), pages 1-23, March.
  3. Lillard, Lee A & Willis, Robert J, 1978. "Dynamic Aspects of Earning Mobility," Econometrica, Econometric Society, vol. 46(5), pages 985-1012, September.
  4. Breusch, T.S. & Pagan, A.R., . "The Lagrange multiplier test and its applications to model specification in econometrics," CORE Discussion Papers RP -412, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Baltagi, Badi H & Griffin, James M, 1988. "A Generalized Error Component Model with Heteroscedastic Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 745-53, November.
  6. 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.
  7. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2006. "Joint LM test for homoskedasticity in a one-way error component model," Journal of Econometrics, Elsevier, vol. 134(2), pages 401-417, October.
  8. Galbraith, John W. & Zinde-Walsh, Victoria, 1995. "Transforming the error-components model for estimation with general ARMA disturbances," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 349-355.
  9. LEJEUNE, Bernard, 1996. "A Full Heteroscedastic One-Way Error Components Model for Incomplete Panel : Maximum Likelihood Estimation and Lagrange Multiplier Testing," CORE Discussion Papers 1996006, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9780521818551.
  11. Yongmiao Hong & Chihwa Kao, 2000. "Wavelet-Based Testing for Serial Correlation of Unknown Form in Panel Models," Center for Policy Research Working Papers 32, Center for Policy Research, Maxwell School, Syracuse University.
  12. repec:fth:louvco:9606 is not listed on IDEAS
  13. 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.
  14. Nilanjana Roy, 2002. "Is Adaptive Estimation Useful For Panel Models With Heteroskedasticity In The Individual Specific Error Component? Some Monte Carlo Evidence," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 189-203.
  15. Baltagi, Badi H. & Li, Qi, 1995. "Testing AR(1) against MA(1) disturbances in an error component model," Journal of Econometrics, Elsevier, vol. 68(1), pages 133-151, July.
  16. Alberto HOLLY & Lucien GARDIOL, 1999. "A Score Test for Individual Heteroscedasticity in a One-way Error Components Model," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 9915, Université de Lausanne, Faculté des HEC, DEEP.
  17. Magnus, Jan R., 1982. "Multivariate error components analysis of linear and nonlinear regression models by maximum likelihood," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 239-285, August.
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Citations

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Cited by:
  1. Montes-Rojas, Gabriel & Sosa-Escudero, Walter, 2011. "Robust tests for heteroskedasticity in the one-way error components model," Journal of Econometrics, Elsevier, vol. 160(2), pages 300-310, February.
  2. 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.
  3. Wu, Jianhong & Li, Guodong, 2014. "Moment-based tests for individual and time effects in panel data models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 569-581.
  4. Juhl, Ted & Sosa-Escudero, Walter, 2014. "Testing for heteroskedasticity in fixed effects models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 484-494.
  5. Pavel Čížek, 2013. "Reweighted least trimmed squares: an alternative to one-step estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 22(3), pages 514-533, September.
  6. Mikko Packalen & Tony Wirjanto, 2010. "Inference about Clustering and Parametric Assumptions in Covariance Matrix Estimation," Working Papers 1012, University of Waterloo, Department of Economics, revised Nov 2010.
  7. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.

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