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Estimating Production Functions with Robustness Against Errors in the Proxy Variables

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  • Guofang Huang
  • Yingyao Hu

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 The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 583.

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Date of creation: Oct 2011
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Handle: RePEc:jhu:papers:583

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  1. 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.
  2. Doraszelski, Ulrich & Jaumandreu, Jordi, 2008. "R&D and Productivity: Estimating Production Functions when Productivity is Endogenous," CEPR Discussion Papers 6636, C.E.P.R. Discussion Papers.
  3. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, 01.
  4. Steve Bond & Måns Söderbom, 2005. "Adjustment costs and the identification of Cobb Douglas production functions," IFS Working Papers W05/04, Institute for Fiscal Studies.
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