On estimating firm-level production functions using proxy variables to control for unobservables
In the common case where polynomial approximations are used for unknown functions, I show how proxy variable approaches to controlling for unobserved productivity, proposed by Olley and Pakes [Olley, S. and Pakes, A., 1996. The dynamics of productivity in the telecommunications equipment industry. Econometrica 64, 1263-1298.] and Levinsohn and Petrin (Levinsohn, J. and Petrin, A., 2003. Estimating production functions using inputs to control for unobservables. Review of Economic Studies 70, 317-341.], can be implemented by specifying different instruments for different equations and applying generalized method of moments. Studying the parameters within a two-equation system clarifies some key identification issues, and joint estimation of the parameters leads to simple inference and more efficient estimators.
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- George S Olley & Ariel Pakes, 1992.
"The Dynamics Of Productivity In The Telecommunications Equipment Industry,"
92-2, Center for Economic Studies, U.S. Census Bureau.
- Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-97, November.
- G. Steven Olley & Ariel Pakes, 1992. "The Dynamics of Productivity in the Telecommunications Equipment Industry," NBER Working Papers 3977, National Bureau of Economic Research, Inc.
- James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Oxford University Press, vol. 70(2), pages 317-341.
- 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.
- 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.
- Ackerberg, Daniel & Caves, Kevin & Frazer, Garth, 2006. "Structural identification of production functions," MPRA Paper 38349, University Library of Munich, Germany.
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