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Adaptive Estimation in the Panel Data Error Component Model with Heteroskedasticity of Unknown Form

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Author Info

  • Stengos, T.
  • Li, Q.

Abstract

The authors show that the adaptive estimation result for the heteroskedasticity of an unknown form time-series (or cross-section) model can be generalized to the panel data error components model. The authors give recursive transformations that change the error term of a random effects model and the first differenced error term of a fixed effects model into classical errors. They also propose a modified Breusch-Pagan test for testing the random individual effects. Monte Carlo evidence suggests that the proposed estimator performs adequately in small samples. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

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Bibliographic Info

Paper provided by University of Guelph, Department of Economics and Finance in its series Working Papers with number 1993-4.

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Length: 25 pages
Date of creation: 1993
Date of revision:
Handle: RePEc:gue:guelph:1993-4

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Keywords: econometrics;

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Cited by:
  1. Georges Bresson & Cheng Hsiao & Alain Pirotte, 2011. "Assessing the contribution of R&D to total factor productivity—a Bayesian approach to account for heterogeneity and heteroskedasticity," AStA Advances in Statistical Analysis, Springer, vol. 95(4), pages 435-452, December.
  2. Baltagi, Badi H. & Jung, Byoung Cheol & Song, Seuck Heun, 2010. "Testing for heteroskedasticity and serial correlation in a random effects panel data model," Journal of Econometrics, Elsevier, vol. 154(2), pages 122-124, February.
  3. Platoni, Silvia & Sckokai, Paolo & Moro, Daniele, 2008. "Panel Data Estimation Techniques for Farm-level Data Model," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44268, European Association of Agricultural Economists.
  4. Badi H. Baltagi & Seuck Heun Song & Jae Hyeok Kwon, 2008. "Testing for Heteroskedasticity and Spatial Correlation in a Random Effects Panel Data Model," Center for Policy Research Working Papers 108, Center for Policy Research, Maxwell School, Syracuse University.
  5. 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.
  6. Eduardo Fé, 2012. "Instrumental variable estimation of heteroskedasticity adaptive error component models," Statistical Papers, Springer, vol. 53(3), pages 577-615, August.
  7. Yiguo Sun & Thanasis Stengos, 2008. "The absolute health income hypothesis revisited: a semiparametric quantile regression approach," Empirical Economics, Springer, vol. 35(2), pages 395-412, September.
  8. Thanasis Stengos & Ximing Wu, 2005. "Partially Adaptive Estimation via Maximum Entropy Densities," University of Cyprus Working Papers in Economics 6-2005, University of Cyprus Department of Economics.
  9. 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.
  10. Werker, B.J.M. & Drost, F.C., 1996. "Closing the GARCH gap: Continuous time GARCH modeling," Open Access publications from Tilburg University urn:nbn:nl:ui:12-72561, Tilburg University.
  11. Juhl, Ted & Sosa-Escudero, Walter, 2014. "Testing for heteroskedasticity in fixed effects models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 484-494.
  12. You, Jinhong & Zhou, Xian & Zhou, Yong, 2010. "Statistical inference for panel data semiparametric partially linear regression models with heteroscedastic errors," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1079-1101, May.

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