IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/72586.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/72586/1/MPRA_paper_72586.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    2. Breitung, Jörg & Salish, Nazarii, 2021. "Estimation of heterogeneous panels with systematic slope variations," Journal of Econometrics, Elsevier, vol. 220(2), pages 399-415.
    3. Alain Pirotte & Jean-Loup Madre, 2011. "Determinants of Urban Sprawl in France," Urban Studies, Urban Studies Journal Limited, vol. 48(13), pages 2865-2886, October.
    4. Carolina Castagnetti & Eduardo Rossi, 2013. "Euro Corporate Bond Risk Factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 372-391, April.
    5. Smith, Ron P. & Tasiran, Ali, 2010. "Random coefficients models of arms imports," Economic Modelling, Elsevier, vol. 27(6), pages 1522-1528, November.
    6. Abonazel, Mohamed R., 2015. "How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models," MPRA Paper 68708, University Library of Munich, Germany.
    7. Yuya Sasaki & Takuya Ura, 2021. "Slow Movers in Panel Data," Papers 2110.12041, arXiv.org.
    8. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
    9. Heather Gibson & Stephen Hall & George Tavlas, 2015. "Are all sovereigns equal? A test of the common determination of sovereign spreads in the euro area," Empirical Economics, Springer, vol. 48(3), pages 939-949, May.
    10. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    11. Georges Bresson & Cheng Hsiao, 2011. "A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 501-529, December.
    12. Castellacci, Fulvio & Natera, Jose Miguel, 2013. "The dynamics of national innovation systems: A panel cointegration analysis of the coevolution between innovative capability and absorptive capacity," Research Policy, Elsevier, vol. 42(3), pages 579-594.
    13. 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;German Statistical Society, vol. 95(4), pages 435-452, December.
    14. Joseph P. Byrne & Alexandros Kontonikas & Alberto Montagnoli, 2013. "International Evidence on the New Keynesian Phillips Curve Using Aggregate and Disaggregate Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(5), pages 913-932, August.
    15. Kazuhiko Hayakawa & Shuichi Nagata & Takashi Yamagata, 2018. "A robust approach to heteroskedasticity, error serial correlation and slope heterogeneity for large linear panel data models with interactive effects," ISER Discussion Paper 1037, Institute of Social and Economic Research, Osaka University.
    16. Abdilahi Ali & Baris Alpaslan, 2013. "Do Migrant Remittances Complement Domestic Investment? New Evidence from Panel Cointegration," Economics Discussion Paper Series 1308, Economics, The University of Manchester.
    17. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2021. "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 18-44, January.
    18. Costantini, Valeria & Martini, Chiara, 2010. "The causality between energy consumption and economic growth: A multi-sectoral analysis using non-stationary cointegrated panel data," Energy Economics, Elsevier, vol. 32(3), pages 591-603, May.
    19. Badi H. Baltagi, 2021. "Dynamic Panel Data Models," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 187-228, Springer.
    20. Ricardo Bebczuk & Tamara Burdisso & Máximo Sangiácomo, 2012. "Credit vs. Payment Services: Financial Development and Economic Activity Revisited," BCRA Working Paper Series 201256, Central Bank of Argentina, Economic Research Department.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:72586. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.