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

  • Richard Blundell

    ()

    (Institute for Fiscal Studies and IFS and UCL)

  • Steve Bond

    ()

    (Institute for Fiscal Studies and Nuffield College, Oxford)

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.

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Paper provided by Institute for Fiscal Studies in its series IFS Working Papers with number W99/04.

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Length: 24 pp.
Date of creation: Feb 1999
Date of revision:
Handle: RePEc:ifs:ifsewp:99/04
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  1. 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.
  2. Nelson, C. & Startz, R., 1988. "Some Furthere Results On The Exact Small Sample Properties Of The Instrumental Variable Estimator," Working Papers 88-06, University of Washington, Department of Economics.
  3. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
  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. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," Harvard Institute of Economic Research Working Papers 1719, Harvard - Institute of Economic Research.
  6. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
  7. 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 S125-40, January.
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