IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/70628.html
   My bibliography  Save this paper

Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects

Author

Listed:
  • Abonazel, Mohamed R.

Abstract

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally, we display new estimators that presented by Youssef and Abonazel (2015) as more efficient estimators than the conventional estimators.

Suggested Citation

  • Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:70628
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/70628/1/MPRA_paper_70628.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bun, Maurice J.G. & Carree, Martin A., 2005. "Bias-Corrected Estimation in Dynamic Panel Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 200-210, April.
    2. Behr, Andreas, 2003. "A comparison of dynamic panel data estimators: Monte Carlo evidence and an application to the investment function," Discussion Paper Series 1: Economic Studies 2003,05, Deutsche Bundesbank.
    3. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
    4. Nelson, Charles R & Startz, Richard, 1990. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 58(4), pages 967-976, July.
    5. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    6. Bun, Maurice J. G. & Kiviet, Jan F., 2003. "On the diminishing returns of higher-order terms in asymptotic expansions of bias," Economics Letters, Elsevier, vol. 79(2), pages 145-152, May.
    7. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    8. 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.
    9. Nelson, Charles R & Startz, Richard, 1990. "The Distribution of the Instrumental Variables Estimator and Its t-Ratio When the Instrument Is a Poor One," The Journal of Business, University of Chicago Press, vol. 63(1), pages 125-140, January.
    10. Hiroaki Chigira & Taku Yamamoto, 2006. "A Bias-Corrected Estimation for Dynamic Panel Models in Small Samples," Hi-Stat Discussion Paper Series d06-177, Institute of Economic Research, Hitotsubashi University.
    11. Stephen Bond & Anke Hoeffler, 2001. "GMM Estimation of Empirical Growth Models," Economics Series Working Papers 2001-W21, University of Oxford, Department of Economics.
    12. Morimune, Kimio, 1983. "Approximate Distributions of k-Class Estimators When the Degree of Overidentifiability Is Large Compared with the Sample Size," Econometrica, Econometric Society, vol. 51(3), pages 821-841, May.
    13. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    14. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Hahn, Jinyong & Hausman, Jerry, 2002. "Notes on bias in estimators for simultaneous equation models," Economics Letters, Elsevier, vol. 75(2), pages 237-241, April.
    16. Abonazel, Mohamed R., 2015. "R-Codes to Calculate GMM Estimations for Dynamic Panel Data Models," MPRA Paper 70627, University Library of Munich, Germany.
    17. 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.
    18. Sevestre, P. & Trognon, A., 1985. "A note on autoregressive error components models," Journal of Econometrics, Elsevier, vol. 28(2), pages 231-245, May.
    19. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
    20. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    21. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    22. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
    23. 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.
    24. Bruno, Giovanni S.F., 2005. "Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models," Economics Letters, Elsevier, vol. 87(3), pages 361-366, June.
    25. Stephen R. Bond, 2002. "Dynamic panel data models: a guide to micro data methods and practice," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 141-162, August.
    26. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    27. Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-681, May.
    28. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    29. S. Liu & H. Neudecker, 1997. "Kantorovich inequalities and efficiency comparisons for several classes of estimators in linear models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 51(3), pages 345-355, November.
    30. Bun, Maurice J.G. & Carree, Martin A., 2006. "Bias-corrected estimation in dynamic panel data models with heteroscedasticity," Economics Letters, Elsevier, vol. 92(2), pages 220-227, August.
    31. 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.
    32. Maddala, G S, 1971. "The Use of Variance Components Models in Pooling Cross Section and Time Series Data," Econometrica, Econometric Society, vol. 39(2), pages 341-358, March.
    33. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    34. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    35. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    36. Beggs, John J. & Nerlove, Marc, 1988. "Biases in dynamic models with fixed effects," Economics Letters, Elsevier, vol. 26(1), pages 29-31.
    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. 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.
    2. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    3. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
    4. Huang, Yongfu, 2010. "Political Institutions and Financial Development: An Empirical Study," World Development, Elsevier, vol. 38(12), pages 1667-1677, December.
    5. Montmartin, Benjamin & Herrera, Marcos, 2015. "Internal and external effects of R&D subsidies and fiscal incentives: Empirical evidence using spatial dynamic panel models," Research Policy, Elsevier, vol. 44(5), pages 1065-1079.
    6. Yongfu Huang, 2005. "Will political liberalisation bring about financial development?," Bristol Economics Discussion Papers 05/578, School of Economics, University of Bristol, UK.
    7. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    8. Roberto Dell'Anno & Adalgiso Amendola, 2015. "Social Exclusion and Economic Growth: An Empirical Investigation in European Economies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(2), pages 274-301, June.
    9. Yongfu Huang, 2011. "Private investment and financial development in a globalized world," Empirical Economics, Springer, vol. 41(1), pages 43-56, August.
    10. Alexander Chudik & M. Hashem Pesaran & Jui‐Chung Yang, 2018. "Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 816-836, September.
    11. Garrone, Paola & Grilli, Luca, 2010. "Is there a relationship between public expenditures in energy R&D and carbon emissions per GDP? An empirical investigation," Energy Policy, Elsevier, vol. 38(10), pages 5600-5613, October.
    12. Christian Merkl & Stephanie Stolz, 2009. "Banks' regulatory buffers, liquidity networks and monetary policy transmission," Applied Economics, Taylor & Francis Journals, vol. 41(16), pages 2013-2024.
    13. Martin Andersson & Hans Lööf, 2011. "Agglomeration and productivity: evidence from firm-level data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(3), pages 601-620, June.
    14. 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 - Working Papers in Economic Theory and Policy 07/2017, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
    15. Armey, Laura E. & McNab, Robert M., 2018. "Expenditure decentralization and natural resources," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 52-61.
    16. Petreski, Marjan, 2009. "Analysis of exchange-rate regime effect on growth: theoretical channels and empirical evidence with panel data," Economics Discussion Papers 2009-49, Kiel Institute for the World Economy (IfW Kiel).
    17. 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.
    18. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    19. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    20. Zier, Patrick, 2013. "Econometric impact assessment of the Common Agricultural Policy in East German agriculture," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 71, number 71.

    More about this item

    Keywords

    Bias-corrected estimators; First-order autoregressive panel model; Generalized method of moments estimators; Kantorovich inequality; Least squares dummy variable estimators.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • 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
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:70628. 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.