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Identification and Estimation of Production Function with Unobserved Heterogeneity

Author

Listed:
  • Paul Schrimpf

    (The University of British Columbia)

  • Michio Suzuki

    (University of Tokyo)

  • Hiroyuki Kasahara

    (University of British Columbia)

Abstract
This paper examines non-parametric identifiability of production function when production functions are heterogenous across firms beyond Hicks-neutral technology terms. Using a finite mixture specification to capture permanent unobserved heterogeneity in production technology, we show that production function for each unobserved type is non-parametrically identified under regularity conditions. We also propose an estimation procedure for production function with random coefficients based on EM algorithm. We estimate a random coefficients production function using the panel data of Japanese publicly-traded manufacturing firms and compare it with the estimate of production function with fixed coefficients estimated by the method of Gandhi, Navarro, and Rivers (2013).

Suggested Citation

  • Paul Schrimpf & Michio Suzuki & Hiroyuki Kasahara, 2015. "Identification and Estimation of Production Function with Unobserved Heterogeneity," 2015 Meeting Papers 924, Society for Economic Dynamics.
    • Handle: RePEc:red:sed015:924
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    Cited by:

    1. Hien Thu Pham & Nhan Buu Phan & Shino Takayama, 2020. "Productivity, Efficiency and Firm Size Distribution: Evidence from Vietnam," Discussion Papers Series 617, School of Economics, University of Queensland, Australia.
    2. Ming Li, 2021. "Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model," Papers 2110.00982, arXiv.org, revised Nov 2024.
    3. Malein, Viktor (Малеин, Виктор) & Ponomarev, Yuriy (Пономарев, Юрий), 2019. "Analysis of Impact of New Technologies in Metallurgy on the Industry Production Function and Total Factor Productivity [Совокупная Факторная Производительность В Черной Металлургии: Влияние Новых Т," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 3, June.
    4. KASAHARA Hiroyuki & NISHIDA Mitsukuni & SUZUKI Michio, 2017. "Decomposition of Aggregate Productivity Growth with Unobserved Heterogeneity," Discussion papers 17083, Research Institute of Economy, Trade and Industry (RIETI).
    5. Emannuel Dhyne & Joep Konings & Joep Konings & Stijn Vanormelingen,, 2018. "IT and productivity: A firm level analysis," Working Paper Research 346, National Bank of Belgium.
    6. Ryo Okui & Takahide Yanagi, 2020. "Kernel estimation for panel data with heterogeneous dynamics," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 156-175.
    7. Tong Li & Yuya Sasaki, 2017. "Constructive Identification of Heterogeneous Elasticities in the Cobb-Douglas Production Function," Papers 1711.10031, arXiv.org.
    8. Nhan Buu Phany & Shino Takayamaz, 2020. "Analyses of Corruption and Productivity with Empirical Study in Vietnam," Discussion Papers Series 628, School of Economics, University of Queensland, Australia.
    9. Konings, Jozef & Dhyne, Emmanuel & Van den bosch, Jeroen & ,, 2018. "The Return on Information Technology: Who Benefits Most?," CEPR Discussion Papers 13246, C.E.P.R. Discussion Papers.
    10. Grieco, Paul & Pinkse, Joris & Slade, Margaret, 2018. "Brewed in North America: Mergers, marginal costs, and efficiency," International Journal of Industrial Organization, Elsevier, vol. 59(C), pages 24-65.
    11. Li, Tong & Sasaki, Yuya, 2024. "Identification of heterogeneous elasticities in gross-output production functions," Journal of Econometrics, Elsevier, vol. 238(2).

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