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Production function estimation in Stata using the Olley and Pakes method

  • Mahmut Yasar


    (Department of Economics, University of Texas at Arlington)

  • Rafal Raciborski

    (Department of Political Science, Emory University)

  • Brian Poi


Productivity is often computed by approximating the weighted sum of the inputs from the estimation of the Cobb-Douglas production function. Such estimates, however, may suffer from simultaneity and selection biases. Olley and Pakes (1996, Econometrica 64: 1263-1297) introduced a semiparametric method that allows us to estimate the production function parameters consistently and thus obtain reliable productivity measures by controlling for such biases. This study first reviews this method and then introduces a Stata command to implement it. We show that when simultaneity and selection biases are not controlled for, the coefficients for the variable inputs are biased upward and the coefficients for the fixed inputs are biased downward. Copyright 2008 by StataCorp LP.

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Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 8 (2008)
Issue (Month): 2 (June)
Pages: 221-231

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Handle: RePEc:tsj:stataj:v:8:y:2008:i:2:p:221-231
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  1. R Blundell & Steven Bond, . "Initial conditions and moment restrictions in dynamic panel data model," Economics Papers W14&104., Economics Group, Nuffield College, University of Oxford.
  2. 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.
  3. 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.
  4. M Arellano & O Bover, 1990. "Another Look at the Instrumental Variable Estimation of Error-Components Models," CEP Discussion Papers dp0007, Centre for Economic Performance, LSE.
  5. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
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