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GMM Bootstrapping and Testing in Dynamic Panels

Author

Listed:
  • Bergström, Pål

    (Trade and Capital Markets)

  • Dahlberg, Matz

    () (Department of Economics)

  • Johansson, Eva

    () (Department of Economics)

Abstract

Two different bootstrap approaches for GMM estimation have recently been suggested for use in dynamic panel data models (Brown & Newey (1995) and Hall & Horowitz (1996)) In this paper we compare the small sample properties of these estimators, suggest how sequential testing can be conducted within the GMM bootstrapping framework, and investigate the performance in a sequence of tests where we seek to find the correct lag length of a dynamic model. This comparison is carried out by means of Monte Carlo experiments. Our findings are that i) the Brown and Newey method has superior size properties, but cannot be used in a sequence of tests without modifications, and that ii) the Hall and Horowitz method works better than the usual asymptotic tests in a sequence of tests.

Suggested Citation

  • Bergström, Pål & Dahlberg, Matz & Johansson, Eva, 1997. "GMM Bootstrapping and Testing in Dynamic Panels," Working Paper Series 1997:10, Uppsala University, Department of Economics.
  • Handle: RePEc:hhs:uunewp:1997_010
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric loss functions and the rationality of expected stock returns," International Journal of Forecasting, Elsevier, vol. 27(2), pages 413-437.
    2. Dahlberg, Matz & Forslund, Anders, 1999. "Direct Displacement Effects of Labour Market Programmes: The Case of Sweden," Working Paper Series 1999:22, Uppsala University, Department of Economics.
    3. Dahlberg, Matz & Forslund, Anders, 1999. "Direct Displacement Effects of Labour Market Programmes: The Case of Sweden," Working Paper Series 1999:22, Uppsala University, Department of Economics.
    4. Edin, Per-Anders & Richardson, Katarina, 2002. " Swimming with the Tide: Solidary Wage Policy and the Gender Earnings Gap," Scandinavian Journal of Economics, Wiley Blackwell, pages 49-67.
    5. Joachim Inkmann, 2000. "Finite Sample Properties of One-Step, Two-Step and Bootstrap Empirical Likelihood Approaches to Efficient GMM Estimation," Econometric Society World Congress 2000 Contributed Papers 0332, Econometric Society.
    6. Griet Malengier & Lorenzo Pozzi, 2005. "Examining Ricardian Equivalence by estimating and bootstrapping a nonlinear dynamic panel model," Money Macro and Finance (MMF) Research Group Conference 2005 61, Money Macro and Finance Research Group.
    7. Bergström, Pål, 1999. "Bootstrap Methods and Applications in Econometrics - A Brief Survey," Working Paper Series 1999:2, Uppsala University, Department of Economics.
    8. Bergström, Pål & Lindberg, Sara, 1998. "Firms' Financial Policy and Labour Demand: Theory and Evidence," Working Paper Series 1998:18, Uppsala University, Department of Economics.
    9. G. Malengier & L. Pozzi, 2004. "Examining Ricardian Equivalence by estimating and bootstrapping a nonlinear dynamic panel model," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/274, Ghent University, Faculty of Economics and Business Administration.
    10. Matz Dahlberg & Eva Johansson, 2000. "An examination of the dynamic behaviour of local governments using GMM bootstrapping methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(4), pages 401-416.

    More about this item

    Keywords

    GMM; bootrapping; sequential tests; panel data;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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