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Univariate Panel Data Models and GMM Estimators: An Exploration Using Real and Simulated Data

Author

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  • Bronwyn H. Hall

    (Nuffield College)

  • Jacques Mairesse

    (INSEE - CREST)

Abstract

This paper explores the time series properties of commonly used variables in firm-level panels: sales (turnover), employment, R\&D, investment, and cash flow. We focus on two questions: 1) whether the behavior of these series is consistent with stationarity, and if so, 2) what order of autoregressive process best describes them. The answer to these two questions is of substantive interest for those who model the dynamic evolution of firms and their behavior. In particular, we are interested in whether firm data is trend stationary (exhibits regression to individual firm means) or difference stationary (evolves as a random walk, possibly with a non-zero drift). We find that estimation of even these very simple processes using fairly large but short panels is fraught with difficulty and we explore the convergence rate of the GMM estimator using simulation methods. We also report the results of using a new class of tests proposed by Im, Pesaran, and Smith for discriminating between stationary and nonstationary processes in medium-sized panels.

Suggested Citation

  • Bronwyn H. Hall & Jacques Mairesse, 2000. "Univariate Panel Data Models and GMM Estimators: An Exploration Using Real and Simulated Data," Econometric Society World Congress 2000 Contributed Papers 1114, Econometric Society.
    • Handle: RePEc:ecm:wc2000:1114
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    References listed on IDEAS

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    1. Bronwyn H. Hall & Jacques Mairesse & Benoit Mulkay, 1998. "Does cash flow cause investment and R&D: an exploration using panel data for French, Japanes and United States scientific firms," IFS Working Papers W98/11, Institute for Fiscal Studies.
    2. Pesaran, M. H. & Shin, Y. & Smith, R. P., 1997. "Pooled Estimation of Long-run Relationships in Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9721, Faculty of Economics, University of Cambridge.
    3. Keane, Michael P & Runkle, David E, 1992. "On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 26-29, January.
    4. 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.
    5. Audretsch,David B. & Thurik,Roy (ed.), 1999. "Innovation, Industry Evolution and Employment," Cambridge Books, Cambridge University Press, number 9780521641661.
    6. Keane, Michael P & Runkle, David E, 1992. "On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 1-9, January.
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    Cited by:

    1. Hyungsik Roger Moon & Peter C. B. Phillips, 2004. "GMM Estimation of Autoregressive Roots Near Unity with Panel Data," Econometrica, Econometric Society, vol. 72(2), pages 467-522, March.
    2. Bronwyn H. Hall & Jacques Mairesse & Benoit Mulkay, 1998. "Does cash flow cause investment and R&D: an exploration using panel data for French, Japanes and United States scientific firms," IFS Working Papers W98/11, Institute for Fiscal Studies.

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