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Estimating production functions with robustness against errors in the proxy variables

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  • Guofang Huang
  • Yingyao Hu

    (Institute for Fiscal Studies and Johns Hopkins University)

Abstract

This paper proposes a new semi-nonparametric maximum likelihood estimation method for estimating production functions. The method extends the literature on structural estimation of production functions, started by the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables. The assumption seems reasonable in many important cases. The new method is straightforward to apply, and a consistent estimate of the asymptotic covariance matrix of the structural parameters can be easily computed.

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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP35/11.

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Date of creation: Nov 2011
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Handle: RePEc:ifs:cemmap:35/11

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  1. Ulrich Doraszelski & Jordi Jaumandreu, 2007. "R&D and productivity : estimating production functions when productivity is endogenous," Economics Working Papers we078652, Universidad Carlos III, Departamento de Economía.
  2. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, 01.
  3. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn, 2011. "Asymptotic Variance Estimator for Two-Step Semiparametric Estimators," Cowles Foundation Discussion Papers 1803, Cowles Foundation for Research in Economics, Yale University.
  4. 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.
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