IDEAS home Printed from https://ideas.repec.org/p/hhs/osloec/2016_019.html
   My bibliography  Save this paper

Panel data estimators and aggregation

Author

Listed:

Abstract

For a panel data regression equation with two-way unobserved heterogeneity, individual-specific and period-specific, ‘within-individual’ and ‘within-period’ estimators, which can be given Ordinary Least Squares (OLS) or Instrumental Variables (IV) interpretations, are considered. A class of estimators defined as linear aggregates of these estimators, is defined. Nine aggregate estimators, including between, within, and Generalized Least Squares (GLS), are special cases. Other estimators are shown to be more robust to simultaneity and measurement error bias than the standard aggregate estimators and more efficient than the ‘disaggregate’ estimators. Empirical illustrations relating to manufacturing productivity are given.

Suggested Citation

  • Biørn, Erik, 2016. "Panel data estimators and aggregation," Memorandum 19/2016, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2016_019
    as

    Download full text from publisher

    File URL: http://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2016/memo-19-2016-2.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fuller, Wayne A. & Battese, George E., 1974. "Estimation of linear models with crossed-error structure," Journal of Econometrics, Elsevier, vol. 2(1), pages 67-78, May.
    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. Lucy F. Ackert & Bryan K. Church & Richard Deaves, 2002. "Bubbles in experimental asset markets: Irrational exuberance no more," FRB Atlanta Working Paper 2002-24, Federal Reserve Bank of Atlanta.
    2. Lucy F. Ackert & Narat Charupat & Bryan K. Church & Richard Deaves, 2006. "Margin, Short Selling, and Lotteries in Experimental Asset Markets," Southern Economic Journal, John Wiley & Sons, vol. 73(2), pages 419-436, October.
    3. Hamid Beladi & Nicholas S. P. Tay & Reza Oladi, 2011. "On Competition for Listings," Working Papers 0003, College of Business, University of Texas at San Antonio.
    4. J. S. Mehta & G. V. L. Narasimham & P. A. V. B. Swamy, 1975. "Estimation of a dynamic demand function for gasoline with different schemes of parameter variation," International Finance Discussion Papers 70, Board of Governors of the Federal Reserve System (U.S.).
    5. Droms, William G. & Walker, David A., 1996. "Mutual fund investment performance," The Quarterly Review of Economics and Finance, Elsevier, vol. 36(3), pages 347-363.
    6. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2016. "Firm‐Level Productivity Spillovers in China's Chemical Industry: A Spatial Hausman‐Taylor Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 214-248, January.
    7. Mullen, John D. & Powell, Roy A. & Reece, B.F., 1980. "The Income And Consumption Experiences Of A Sample Of Farm Families," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 24(3), pages 1-15, December.
    8. Rachel A. Rosenfeld & Franã‡Ois Nielsen, 1984. "Inequality and Careers," Sociological Methods & Research, , vol. 12(3), pages 279-321, February.
    9. George Frisvold & Kevin Ingram, 1995. "Sources of agricultural productivity growth and stagnation in sub‐Saharan Africa," Agricultural Economics, International Association of Agricultural Economists, vol. 13(1), pages 51-61, October.
    10. Badi H. Baltagi, 1999. "Specification Tests in Panel Data Models Using Artificial Regressions," Annals of Economics and Statistics, GENES, issue 55-56, pages 277-297.
    11. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
    12. Wu, Jie & Zahoor, Nadia & Khan, Zaheer & Meyer, Martin, 2023. "The effects of inward FDI communities on the research and development intensity of emerging market locally domiciled firms: Partial foreign ownership as a contingency," Journal of Business Research, Elsevier, vol. 156(C).
    13. repec:zbw:bofrdp:1993_001 is not listed on IDEAS
    14. Robert T. Clair & Joanna Kolson & Kenneth J. Robinson, 1995. "The Texas banking crisis and the payments system," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q I, pages 13-21.
    15. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    16. Ron Kaniel & Shuming Liu & Gideon Saar & Sheridan Titman, 2012. "Individual Investor Trading and Return Patterns around Earnings Announcements," Journal of Finance, American Finance Association, vol. 67(2), pages 639-680, April.
    17. Peracchi, Franco & Welch, Finis, 1995. "How representative are matched cross-sections? Evidence from the Current Population Survey," Journal of Econometrics, Elsevier, vol. 68(1), pages 153-179, July.
    18. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    19. Kang, Jun-Koo & Stulz, Rene M., 1997. "Why is there a home bias? An analysis of foreign portfolio equity ownership in Japan," Journal of Financial Economics, Elsevier, vol. 46(1), pages 3-28, October.
    20. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2011. "Recent trends in trading activity and market quality," Journal of Financial Economics, Elsevier, vol. 101(2), pages 243-263, August.
    21. Richard J. Buttimer, Jr. & Ronald C. Rutherford & Ron Witten, 1997. "Industrial Warehouse Rent Determinants in the Dallas/Fort Worth Area," Journal of Real Estate Research, American Real Estate Society, vol. 13(1), pages 47-56.

    More about this item

    Keywords

    Panel data; Aggregation; IV estimation; Robustness; Method of moments; Factor productivity;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

    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:hhs:osloec:2016_019. 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: Mari Strønstad Øverås (email available below). General contact details of provider: https://edirc.repec.org/data/souiono.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.