IDEAS home Printed from https://ideas.repec.org/p/ucr/wpaper/202209.html
   My bibliography  Save this paper

Weighted Average Estimation in Panel Data

Author

Listed:
  • Ali Mehrabani

    (Southern Illinois University)

  • Aman Ullah

    (Department of Economics, University of California Riverside)

Abstract

Mukhtar M. Ali has made many innovative and influential contributions in different areas of economics, finance, econometrics, and statistics. His contributions include developing econometric models to examine the determinants of the demand for casino gaming, investigating the approximate and exact distribution and moments of various econometric estimators and test statistics, and studying the statistical properties of time series based statistics under stationary and non-stationary processes (for example, see Ali and Thalheimer (1983, 2008), Ali (1977, 1979, 1984, 1989), Ali and Sharma (1993, 1996), Tsui and Ali (1992, 2002), Ali and Giaccotto (1982a, 1982b, 1984), Ali and Tiao (1971), and Ali and Silver (1985, 1989), among others). All of these have made significant impact on the profession and have been instrumental in advancing further research in statistics and econometrics. In this paper, we study the approximate rst two moments of two weighted average estimators of the slope parameters in linear panel data models. The weighted average estimators shrink a generalized least squares estimator towards a restricted generalized least squares estimator, where the restrictions represent possible parameter specifications. The averaging weight is inversely proportional to a weighted quadratic loss function. The approximate bias and second moment matrix of the weighted average estimators using the large-sample approximations are provided. We give the conditions under which the weighted average estimators dominate the generalized least squares estimator on the basis of their mean squared errors.

Suggested Citation

  • Ali Mehrabani & Aman Ullah, 2022. "Weighted Average Estimation in Panel Data," Working Papers 202209, University of California at Riverside, Department of Economics, revised Apr 2022.
    • Handle: RePEc:ucr:wpaper:202209
    as

    Download full text from publisher

    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202209.pdf
    File Function: First version, 2022
    Download Restriction: no
    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202209R.pdf
    File Function: Revised version, 2022
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ali, Mukhtar M. & Sharma, Subhash C., 1993. "Robustness to nonnormality of the Durbin-Watson test for autocorrelation," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 117-136.
    2. G. S. Maddala & Hongyi Li & V. K. Srivastava, 2001. "A Comparative Study of Different Shrinkage Estimators for Panel Data Models," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 1-30, May.
    3. Hansen, Bruce E., 2016. "Efficient shrinkage in parametric models," Journal of Econometrics, Elsevier, vol. 190(1), pages 115-132.
    4. Richard Thalheimer & Mukhtar M. Ali, 2008. "The Demand For Casino Gaming With Special Reference To A Smoking Ban," Economic Inquiry, Western Economic Association International, vol. 46(2), pages 273-282, April.
    5. Silver, J. Lew & Ali, Mukhtar M., 1989. "Testing Slutsky symmetry in systems of linear demand equations," Journal of Econometrics, Elsevier, vol. 41(2), pages 251-266, June.
    6. Ali, Mukhtar M. & Giaccotto, Carmelo, 1984. "A study of several new and existing tests for heteroscedasticity in the general linear model," Journal of Econometrics, Elsevier, vol. 26(3), pages 355-373, December.
    7. Jan R. Magnus & Dmitry Danilov, 2004. "Forecast accuracy after pretesting with an application to the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 251-274.
    8. Leeb, Hannes & Pötscher, Benedikt M., 2003. "The Finite-Sample Distribution Of Post-Model-Selection Estimators And Uniform Versus Nonuniform Approximations," Econometric Theory, Cambridge University Press, vol. 19(1), pages 100-142, February.
    9. Ali Mehrabani & Aman Ullah, 2020. "Improved Average Estimation in Seemingly Unrelated Regressions," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    10. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, number 9780198774488.
    11. Jan R. Magnus & J. Durbin, 1999. "Estimation of Regression Coefficients of Interest When Other Regression Coefficients Are of No Interest," Econometrica, Econometric Society, vol. 67(3), pages 639-644, May.
    12. Giaccotto, Carmelo & Ali, Mukhtar M, 1982. "Optimum Distribution-Free Tests and Further Evidence of Heteroscedasticity in the Market Model," Journal of Finance, American Finance Association, vol. 37(5), pages 1247-1257, December.
    13. Jan R. Magnus, 2002. "Estimation of the mean of a univariate normal distribution with known variance," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 225-236, June.
    14. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
    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. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2018. "Weighted-average least squares estimation of generalized linear models," Journal of Econometrics, Elsevier, vol. 204(1), pages 1-17.
    2. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
    3. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    4. Jan R. Magnus & Dmitry Danilov, 2004. "Forecast accuracy after pretesting with an application to the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 251-274.
    5. Clarke, Judith A., 2008. "On weighted estimation in linear regression in the presence of parameter uncertainty," Economics Letters, Elsevier, vol. 100(1), pages 1-3, July.
    6. Magnus, Jan & Peresetsky, Anatoly, 2010. "The price of Moscow apartments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 17(1), pages 89-105.
    7. Judith Anne Clarke, 2017. "Model Averaging OLS and 2SLS: An Application of the WALS Procedure," Econometrics Working Papers 1701, Department of Economics, University of Victoria.
    8. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
    9. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
    10. Magnus, Jan R. & Powell, Owen & Prüfer, Patricia, 2010. "A comparison of two model averaging techniques with an application to growth empirics," Journal of Econometrics, Elsevier, vol. 154(2), pages 139-153, February.
    11. Zou, Guohua & Wan, Alan T.K. & Wu, Xiaoyong & Chen, Ti, 2007. "Estimation of regression coefficients of interest when other regression coefficients are of no interest: The case of non-normal errors," Statistics & Probability Letters, Elsevier, vol. 77(8), pages 803-810, April.
    12. Einmahl, J.H.J. & Magnus, J.R. & Kumar, K., 2011. "On the Choice of Prior in Bayesian Model Averaging," Discussion Paper 2011-003, Tilburg University, Center for Economic Research.
    13. Magnus, J.R. & Powell, O.R. & Prüfer, P., 2008. "A Comparison of Two Averaging Techniques with an Application to Growth Empirics," Other publications TiSEM 0392dffa-51e0-4bc9-9644-f, Tilburg University, School of Economics and Management.
    14. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    15. Aedın Doris & Donal O’Neill & Olive Sweetman, 2011. "GMM estimation of the covariance structure of longitudinal data on earnings," Stata Journal, StataCorp LP, vol. 11(3), pages 439-459, September.
    16. Becker William & Paruolo Paolo & Saltelli Andrea, 2021. "Variable Selection in Regression Models Using Global Sensitivity Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 13(2), pages 187-233, July.
    17. Zhang, Xinyu & Yu, Jihai, 2018. "Spatial weights matrix selection and model averaging for spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 203(1), pages 1-18.
    18. Karen Poghosyan & Jan R. Magnus, 2012. "WALS Estimation and Forecasting in Factor-based Dynamic Models with an Application to Armenia," International Econometric Review (IER), Econometric Research Association, vol. 4(1), pages 40-58, April.
    19. Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics.
    20. Dardanoni, Valentino & Modica, Salvatore & Peracchi, Franco, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Journal of Econometrics, Elsevier, vol. 162(2), pages 362-368, June.

    More about this item

    Keywords

    Asymptotic approximations; xed-e ects; panel data; random-e ects; Stein-like shrinkage estimator.;
    All these keywords.

    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:ucr:wpaper:202209. 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: Kelvin Mac (email available below). General contact details of provider: https://edirc.repec.org/data/deucrus.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.