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Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties

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  • Abonazel, Mohamed R.

Abstract

This paper provides a generalized model for the random-coefficients panel data model where the errors are cross-sectional heteroskedastic and contemporaneously correlated as well as with the first-order autocorrelation of the time series errors. Of course, the conventional estimators, which used in standard random-coefficients panel data model, are not suitable for the generalized model. Therefore, the suitable estimator for this model and other alternative estimators have been provided and examined in this paper. Moreover, the efficiency comparisons for these estimators have been carried out in small samples and also we examine the asymptotic distributions of them. The Monte Carlo simulation study indicates that the new estimators are more reliable (more efficient) than the conventional estimators in small samples.

Suggested Citation

  • Abonazel, Mohamed R., 2016. "Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties," MPRA Paper 72586, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:72586
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    References listed on IDEAS

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    1. Joakim Westerlund & Paresh Narayan, 2015. "A Random Coefficient Approach to the Predictability of Stock Returns in Panels," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 605-664.
    2. Feige, Edgar L & Swamy, P A V B, 1974. "A Random Coefficient Model of the Demand for Liquid Assets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 6(2), pages 241-252, May.
    3. Youssef, Ahmed H. & El-Sheikh, Ahmed A. & Abonazel, Mohamed R., 2014. "Improving the Efficiency of GMM Estimators for Dynamic Panel Models," MPRA Paper 68675, University Library of Munich, Germany.
    4. Brian P. Poi, 2003. "From the help desk: Swamy's random-coefficients model," Stata Journal, StataCorp LP, vol. 3(3), pages 302-308, September.
    5. V. V. Anh & T. Chelliah, 1999. "Estimated Generalized Least Squares for Random Coefficient Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(1), pages 31-46, March.
    6. Patrick Sevestre & Laszlo Matyas, 2008. "The Econometrics of Panel Data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279977, HAL.
    7. Swamy, P.A.V.B. & Mehta, J.S. & Tavlas, G.S. & Hall, S.G., 2015. "Two applications of the random coefficient procedure: Correcting for misspecifications in a small area level model and resolving Simpson's paradox," Economic Modelling, Elsevier, vol. 45(C), pages 93-98.
    8. Rao, U. L. Gouranga, 1982. "A note on the unbiasedness of Swamy's estimator for the random coefficient regression model," Journal of Econometrics, Elsevier, vol. 18(3), pages 395-401, April.
    9. Boot, John C. G. & Frankfurter, George M., 1972. "The Dynamics of Corporate Debt Management, Decision Rules, and Some Empirical Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(4), pages 1957-1965, September.
    10. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    11. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    12. Donald T. Sant, 1977. "Generalized Least Squares Applied to Time Varying Parameter Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 6, number 3, pages 301-314, National Bureau of Economic Research, Inc.
    13. Juan Alcacer & Wilbur Chung & Ashton Hawk & Gonnalo Pacheco-De-Almeida, 2013. "Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs," Working Papers hal-01970729, HAL.
    14. Youssef, Ahmed H. & El-Sheikh, Ahmed A. & Abonazel, Mohamed R., 2014. "New GMM Estimators for Dynamic Panel Data Models," MPRA Paper 68676, University Library of Munich, Germany.
    15. Swamy, P A V B, 1970. "Efficient Inference in a Random Coefficient Regression Model," Econometrica, Econometric Society, vol. 38(2), pages 311-323, March.
    16. Thomas F. Cooley & Edward C. Prescott, 1973. "Systematic (Non-Random) Variation Models Varying Parameter Regression: A Theory And Some Applications," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 2, number 4, pages 463-473, National Bureau of Economic Research, Inc.
    17. Horváth, Lajos & Trapani, Lorenzo, 2016. "Statistical inference in a random coefficient panel model," Journal of Econometrics, Elsevier, vol. 193(1), pages 54-75.
    18. Boness, A James & Frankfurter, George M, 1977. "Evidence of Non-Homogeneity of Capital Costs within "Risk-Classes."," Journal of Finance, American Finance Association, vol. 32(3), pages 775-787, June.
    19. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    20. Beck, Nathaniel & Katz, Jonathan N., 2007. "Random Coefficient Models for Time-Series—Cross-Section Data: Monte Carlo Experiments," Political Analysis, Cambridge University Press, vol. 15(2), pages 182-195, April.
    21. Mousa, Amani & Youssef, Ahmed H. & Abonazel, Mohamed R., 2011. "A Monte Carlo Study for Swamy’s Estimate of Random Coefficient Panel Data Model," MPRA Paper 49768, University Library of Munich, Germany.
    22. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2008. "Fixed effects instrumental variables estimation in correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 539-552, January.
    23. Youssef, Ahmed H. & Abonazel, Mohamed R., 2009. "A Comparative Study for Estimation Parameters in Panel Data Model," MPRA Paper 49713, University Library of Munich, Germany.
    24. Dwivedi, T. D. & Srivastava, V. K., 1978. "Optimality of least squares in the seemingly unrelated regression equation model," Journal of Econometrics, Elsevier, vol. 7(3), pages 391-395, April.
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    More about this item

    Keywords

    Classical pooling estimation; Contemporaneous covariance; First-order autocorrelation; Heteroskedasticity; Mean group estimation; Monte Carlo simulation; Random coefficient regression.;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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