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

50 Pages Posted: 15 Feb 2017

Date Written: February 11, 2017

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.

Keywords: Production Functions, Productivity, Prodest, MrEst, Dynamic Panel GMM

Suggested Citation

Mollisi, Vincenzo and Rovigatti, Gabriele, Theory and Practice of TFP Estimation: The Control Function Approach Using Stata (February 11, 2017). CEIS Working Paper No. 399, Available at SSRN: https://ssrn.com/abstract=2916753 or http://dx.doi.org/10.2139/ssrn.2916753

Vincenzo Mollisi

University of Rome Tor Vergata ( email )

Via di Tor Vergata
Rome, Lazio 00133
Italy

Gabriele Rovigatti (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

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