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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.

Suggested Citation

  • Guofang Huang & Yingyao Hu, 2011. "Estimating Production Functions with Robustness Against Errors in the Proxy Variables," Economics Working Paper Archive 583, The Johns Hopkins University,Department of Economics.
    • Handle: RePEc:jhu:papers:583
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    Cited by:

    1. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Proxy variable estimation of productivity and efficiency," Southern Economic Journal, John Wiley & Sons, vol. 89(3), pages 885-923, January.
    2. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    3. Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 471-496, September.
    4. Tong Li & Yuya Sasaki, 2017. "Constructive Identification of Heterogeneous Elasticities in the Cobb-Douglas Production Function," Papers 1711.10031, arXiv.org.
    5. Alvaro Aguirre & Matias Tapia & Lucciano Villacorta, 2021. "Production, Investment and Wealth Dynamics under Financial Frictions: An Empirical Investigation of the Selffinancing Channel," Working Papers Central Bank of Chile 904, Central Bank of Chile.
    6. Hu, Yingyao & Huang, Guofang & Sasaki, Yuya, 2020. "Estimating production functions with robustness against errors in the proxy variables," Journal of Econometrics, Elsevier, vol. 215(2), pages 375-398.
    7. Chen Yeh & Claudia Macaluso & Brad Hershbein, 2022. "Monopsony in the US Labor Market," American Economic Review, American Economic Association, vol. 112(7), pages 2099-2138, July.
    8. Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Working Papers wp2018_1703, CEMFI.
    9. Kim, Kyoo il & Petrin, Amil & Song, Suyong, 2016. "Estimating production functions with control functions when capital is measured with error," Journal of Econometrics, Elsevier, vol. 190(2), pages 267-279.
    10. Abito, Jose Miguel, 2019. "Estimating Production Functions with Fixed Effects," MPRA Paper 97825, University Library of Munich, Germany.
    11. Chen, Hanxue & Wang, Shuhong & Song, Malin, 2021. "Global Environmental Value Chain Embeddedness and Enterprise Production Efficiency Improvement," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 278-290.
    12. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.
    13. Dibyendu Maiti & Chiranjib Neogi, 2020. "Endogeneity Corrected Stochastic Frontier with Market Imperfections," Working papers 313, Centre for Development Economics, Delhi School of Economics.
    14. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    15. Ainagul T. Mamyralieva & Aziza B. Karbekova & Gulchehra B. Abdyrahmanova, 2022. "Analysis of the economic sectors? sustainability of the Kyrgyz Republic," RIVISTA DI STUDI SULLA SOSTENIBILITA', FrancoAngeli Editore, vol. 0(2), pages 185-204.
    16. Emir Malikov & Jingfang Zhang & Shunan Zhao & Subal C. Kumbhakar, 2023. "Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity," Papers 2302.13430, arXiv.org.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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