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GMM Estimation of Empirical Growth Models

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  • Stephen Bond
  • Anke Hoeffler
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    Abstract

    This paper highlights a problem in using the first-differenced GMM panel data estimator to estimate cross-country growth regressions. When the time series are persistent, the first-differenced GMM estimator can be poorly behaved, since lagged levels of the series provide only weak instruments for subsequent first-differences. Revisiting the work of Caselli, Esquivel and Lefort (1996), we show that this problem may be serious in practice. We suggest using a more efficient GMM estimator that exploits stationarity restrictions, and this approach is shown to give more reasonable results than first-differenced GMM in our estimation of an empirical growth model.

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    File URL: http://www.nuff.ox.ac.uk/economics/papers/2001/w21/bht10.pdf
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    Bibliographic Info

    Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2001-W21.

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    Date of creation: 01 Sep 2001
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    Handle: RePEc:oxf:wpaper:2001-w21

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    Related research

    Keywords: convergence; growth; generalized method of moments; weak instruments;

    References

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    1. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 17(1), pages 36-49, January.
    2. Lee, Kevin & Pesaran, M Hashem & Smith, Ron, 1997. "Growth and Convergence in Multi-country Empirical Stochastic Solow Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 12(4), pages 357-92, July-Aug..
    3. Guido W. Imbens & Phillip Johnson & Richard H. Spady, 1995. "Information Theoretic Approaches to Inference in Moment Condition Models," NBER Technical Working Papers 0186, National Bureau of Economic Research, Inc.
    4. Richard Blundell & Steve Bond, 1999. "GMM estimation with persistent panel data: an application to production functions," IFS Working Papers, Institute for Fiscal Studies W99/04, Institute for Fiscal Studies.
    5. Nelson, Charles R & Startz, Richard, 1990. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," Econometrica, Econometric Society, Econometric Society, vol. 58(4), pages 967-76, July.
    6. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, Econometric Society, vol. 65(3), pages 557-586, May.
    7. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 94(5), pages 1002-37, October.
    8. Kevin Lee & M. Hashem Pesaran & Ron Smith, . "Growth and Convergence in a Multi-County empirical Stochastic Solow Model," Discussion Papers in Economics, Department of Economics, University of Leicester 96/14, Department of Economics, University of Leicester.
    9. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, Econometric Society, vol. 56(6), pages 1371-95, November.
    10. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 14(3), pages 262-80, July.
    11. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, Elsevier, vol. 68(1), pages 29-51, July.
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