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Theory and Practice of TFP Estimation: the Control Function Approach Using Stata

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Abstract

Alongside Instrumental Variable (IV) and Fixed Effects (FE), the Control Function (CF) approach is the most widely used in production function estimation. Olley-Pakes, Levinsohn-Petrin, Ackerberg-Caves-Frazer have all contributed to the literature proposing two-steps estimation procedures, while Wooldridge showed how to perform a consistent estimation within a single step GMM framework. In this paper we propose a new estimator, based on Wooldridge's, using dynamic panel instruments à la Blundell-Bond and we evaluate its performance by Monte Carlo simulations. We also present a new Stata module - prodest - for production function estimation, show its main features and key strengths in a comparative analysis with other available user-written commands. Lastly, we provide evidence of the numerical challenges faced when using OP/LP estimators with ACF correction in empirical applications and document how the GMM estimates vary depending on the optimization/starting points used.

Suggested Citation

  • Vincenzo Mollisi & Gabriele Rovigatti, 2017. "Theory and Practice of TFP Estimation: the Control Function Approach Using Stata," CEIS Research Paper 399, Tor Vergata University, CEIS, revised 14 Feb 2017.
    • Handle: RePEc:rtv:ceisrp:399
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    References listed on IDEAS

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    1. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Papers 2005-W04, Economics Group, Nuffield College, University of Oxford.
    2. 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.
    3. Christopher R. Knittel & Konstantinos Metaxoglou, 2014. "Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 34-59, March.
    4. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    5. Amil Petrin & Brian P. Poi & James Levinsohn, 2004. "Production function estimation in Stata using inputs to control for unobservables," Stata Journal, StataCorp LP, vol. 4(2), pages 113-123, June.
    6. Mahmut Yasar & Rafal Raciborski & Brian Poi, 2008. "Production function estimation in Stata using the Olley and Pakes method," Stata Journal, StataCorp LP, vol. 8(2), pages 221-231, June.
    7. Marc J. Melitz, 2003. "The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity," Econometrica, Econometric Society, vol. 71(6), pages 1695-1725, November.
    8. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    9. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    10. Wooldridge, Jeffrey M., 2009. "On estimating firm-level production functions using proxy variables to control for unobservables," Economics Letters, Elsevier, vol. 104(3), pages 112-114, September.
    11. Wooldridge, Jeffrey M., 1996. "Estimating systems of equations with different instruments for different equations," Journal of Econometrics, Elsevier, vol. 74(2), pages 387-405, October.
    12. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Series Working Papers 2005-W04, University of Oxford, Department of Economics.
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