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GMM estimation with persistent panel data: an application to production functions

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  • Richard Blundell

    () (Institute for Fiscal Studies and Institute for Fiscal Studies and University College London)

  • Stephen Bond

    () (Institute for Fiscal Studies and Nuffield College, Oxford)

Abstract

We consider the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. Standard GMM estimators, which eliminate unobserved firm-specific eects by taking first differences, have been found to produce unsatisfactory results in this context (Mairesse and Hall, 1996). We attribute this to weak instruments: the series on rm sales, capital and employment are highly persistent, so that lagged levels are only weakly correlated with subsequent first differences. As shown in Blundell and Bond (1998), this can result in large finite-sample biases when using the standard first-differenced GMM estimator. Blundell and Bond (1998) also show that these biases can be dramatically reduced by exploiting reasonable stationarity restrictions on the initial conditions process. This yields an extended GMM estimator in which lagged first-differences of the series are also used as instruments for the levels equations (cf. Arellano and Bover, 1995). Using data for a panel of R&D-performing US manufacturing companies, similar to that in Mairesse and Hall (1996), we show that the instruments available for the production function in first differences are indeed weak. We find that the additional instruments used in our extended GMM estimator appear to be both valid and informative in this context; this estimator yields much more reasonable parameter estimates. We also stress the importance of allowing for an autoregressive component in the productivity shocks.

Suggested Citation

  • Richard Blundell & Stephen Bond, 1999. "GMM estimation with persistent panel data: an application to production functions," IFS Working Papers W99/04, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:ifsewp:99/04
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    References listed on IDEAS

    as
    1. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
    2. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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