Theory and practice of total-factor productivity estimation: The control function approach using Stata
Gabriele Rovigatti
University of Chicago
Booth School of Business
Chicago, IL
[email protected]
|
Vincenzo Mollisi
Free University of Bozen–Bolzano
Faculty of Economics and Management
Bolzano, Italy
[email protected]
|
Abstract. Alongside instrumental-variables and fixed-effects approaches, the control
function approach is the most widely used in production function estimation.
Olley and Pakes (1996, Econometrica 64: 1263–1297), Levinsohn and
Petrin (2003, Review of Economic Studies 70: 317–341), and
Ackerberg, Caves, and Frazer (2015, Econometrica 83: 2411–2451)
have all contributed to the field by proposing two-step estimation procedures,
whereas Wooldridge (2009, Economics Letters 104: 112–114) showed
how to perform a consistent estimation within a single-step generalized method
of moments framework. In this article, we propose a new estimator based on
Wooldridge's estimation procedure, using dynamic panel instruments `a la
Blundell and Bond (1998, Journal of Econometrics 87: 115–143), and
we evaluate its performance by using Monte Carlo simulations. We also present
the new command prodest for production function estimation, and we show its
main features and strengths in a comparative analysis with other
community-contributed commands. Finally, we provide evidence of the numerical
challenges faced when using the Olley–Pakes and Levinsohn–Petrin
estimators with the Ackerberg–Caves–Frazer correction in empirical
applications, and we document how the generalized method of moments estimates
vary depending on the optimizer or starting points used.
View all articles by these authors:
Gabriele Rovigatti, Vincenzo Mollisi
View all articles with these keywords:
prodest, production functions, productivity, MrEst, dynamic panel GMM
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
