IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v51y2016i1d10.1007_s00181-015-1000-5.html
   My bibliography  Save this article

A Monte Carlo study of the BE estimator for growth regressions

Author

Listed:
  • Jan Ditzen

    () (Heriot-Watt University)

  • Erich Gundlach

    () (Hamburg University and GIGA German Institute of Global and Area Studies)

Abstract

Abstract A recent Monte Carlo study claims that the BE estimator outperforms other panel estimators in terms of average estimation bias in a dynamic specification of the Solow model in levels (Hauk and Wacziarg in J Econ Growth 14(2):103–147, 2009). Our simulation results show that the reported performance of the BE estimator depends on the selected parameterization of the data generating process. Using alternative parameter values, a different model specification, and a restricted cross-section estimator, we find that the BE estimator tends to produce a coefficient of the lagged endogenous variable that is biased toward 1.

Suggested Citation

  • Jan Ditzen & Erich Gundlach, 2016. "A Monte Carlo study of the BE estimator for growth regressions," Empirical Economics, Springer, vol. 51(1), pages 31-55, August.
  • Handle: RePEc:spr:empeco:v:51:y:2016:i:1:d:10.1007_s00181-015-1000-5
    DOI: 10.1007/s00181-015-1000-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-015-1000-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Coakley, Jerry & Fuertes, Ana-Maria & Smith, Ron, 2006. "Unobserved heterogeneity in panel time series models," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2361-2380, May.
    2. 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.
    3. Kevin Lee & M. Hashem Pesaran & Ron Smith, 1998. "Growth Empirics: A Panel Data Approach—A Comment," The Quarterly Journal of Economics, Oxford University Press, vol. 113(1), pages 319-323.
    4. Pesaran, M.H., 2004. "‘General Diagnostic Tests for Cross Section Dependence in Panels’," Cambridge Working Papers in Economics 0435, Faculty of Economics, University of Cambridge.
    5. N. Gregory Mankiw & David Romer & David N. Weil, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 107(2), pages 407-437.
    6. William Hauk & Romain Wacziarg, 2009. "A Monte Carlo study of growth regressions," Journal of Economic Growth, Springer, vol. 14(2), pages 103-147, June.
    7. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    8. Baltagi, Badi H & Griffin, James M, 1984. "Short and Long Run Effects in Pooled Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(3), pages 631-645, October.
    9. Nazrul Islam, 1995. "Growth Empirics: A Panel Data Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 110(4), pages 1127-1170.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Reed, W. Robert & Zhu, Min, 2017. "On estimating long-run effects in models with lagged dependent variables," Economic Modelling, Elsevier, vol. 64(C), pages 302-311.
    2. Jan Ditzen, 2016. "xtdcce: Estimating Dynamic Common Correlated Effects in Stata," SEEC Discussion Papers 1601, Spatial Economics and Econometrics Centre, Heriot Watt University.
    3. Hauk, William R., 2017. "Endogeneity bias and growth regressions," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 143-161.
    4. Kufenko, Vadmin & Prettner, Klaus, 2017. "You can't always get what you want? A Monte Carlo analysis of the bias and the efficiency of dynamic panel data estimators," ECON WPS - Vienna University of Technology Working Papers in Economic Theory and Policy 07/2017, Vienna University of Technology, Institute for Mathematical Methods in Economics, Research Group Economics (ECON).

    More about this item

    Keywords

    Monte Carlo simulations; Dynamic panel specification; BE estimator; Solow model; Convergence rate;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    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:spr:empeco:v:51:y:2016:i:1:d:10.1007_s00181-015-1000-5. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.