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GMM Estimation of Empirical Growth Models

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  • Bond, Stephen Roy
  • Hoeffler, Anke
  • Temple, Jonathan

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.

Suggested Citation

  • Bond, Stephen Roy & Hoeffler, Anke & Temple, Jonathan, 2001. "GMM Estimation of Empirical Growth Models," CEPR Discussion Papers 3048, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:3048
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    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Alok Bhargava & J. D. Sargan, 2006. "Estimating Dynamic Random Effects Models From Panel Data Covering Short Time Periods," World Scientific Book Chapters, in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 1, pages 3-27, World Scientific Publishing Co. Pte. Ltd..
    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. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    5. 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.
    6. 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.
    7. Richard Blundell & Stephen Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
    8. repec:adr:anecst:y:1999:i:55-56:p:16 is not listed on IDEAS
    9. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    10. Ross Levine & Norman Loayza & Thorsten Beck, 2002. "Financial Intermediation and Growth: Causality and Causes," Central Banking, Analysis, and Economic Policies Book Series, in: Leonardo Hernández & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Se (ed.),Banking, Financial Integration, and International Crises, edition 1, volume 3, chapter 2, pages 031-084, Central Bank of Chile.
    11. 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.
    12. 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., vol. 12(4), pages 357-392, July-Aug..
    13. 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.
    14. N. Gregory Mankiw & David Romer & David N. Weil, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(2), pages 407-437.
    15. 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.
    16. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    17. Benhabib, Jess & Spiegel, Mark M, 2000. "The Role of Financial Development in Growth and Investment," Journal of Economic Growth, Springer, vol. 5(4), pages 341-360, December.
    18. Guido W. Imbens & Richard H. Spady & Phillip Johnson, 1998. "Information Theoretic Approaches to Inference in Moment Condition Models," Econometrica, Econometric Society, vol. 66(2), pages 333-358, March.
    19. Easterly, William & Loayza, Norman & Montiel, Peter, 1997. "Has Latin America's post-reform growth been disappointing?," Journal of International Economics, Elsevier, vol. 43(3-4), pages 287-311, November.
    20. Robert E. Hall & Charles I. Jones, 1999. "Why do Some Countries Produce So Much More Output Per Worker than Others?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 83-116.
    21. 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.
    22. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    23. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    24. Benhabib, Jess & Spiegel, Mark, 1997. "Cross-Country Growth Regressions," Working Papers 97-20, C.V. Starr Center for Applied Economics, New York University.
    25. Caselli, Francesco & Esquivel, Gerardo & Lefort, Fernando, 1996. "Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics," Journal of Economic Growth, Springer, vol. 1(3), pages 363-389, September.
    26. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    27. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
    28. Peter J. Klenow & Andrés Rodríguez-Clare, 1997. "The Neoclassical Revival in Growth Economics: Has It Gone Too Far?," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 73-114, National Bureau of Economic Research, Inc.
    29. 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.
    30. Kristin J. Forbes, 2000. "A Reassessment of the Relationship between Inequality and Growth," American Economic Review, American Economic Association, vol. 90(4), pages 869-887, September.
    31. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
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    More about this item

    Keywords

    Convergence; Growth; Generalized method of moments; Weak instruments;
    All these keywords.

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth 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

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