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Correcting for Measurement Error in a Stochastic Frontier Model: A Fishery Context

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  • Burns, Christopher

Abstract

Using data from the Mid-Atlantic surfclam fishery, this study examines the effect of measurement error on the analysis of technical efficiency. After specifying a stochastic frontier model and estimating it under naive analysis, a measurement error correction technique known as Simulation Extrapolation (SIMEX) is used to obtain less biased estimates of technical efficiency and production parameters. The SIMEX estimates of the stochastic frontier model agree with economic theory and show that important relationships between technical efficiency and vessel characteristics are present, something the naive estimates do not. Both sets of estimates show regional variation in technical efficiency, possibly due to declining landings per-uniteffort, suggesting future fishery management should take this into account.

Suggested Citation

  • Burns, Christopher, 2013. "Correcting for Measurement Error in a Stochastic Frontier Model: A Fishery Context," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150499, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea13:150499
    DOI: 10.22004/ag.econ.150499
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    File URL: https://ageconsearch.umn.edu/record/150499/files/AAEA%20finalBurns.pdf
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    References listed on IDEAS

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    1. Brandt, Sylvia, 2007. "Evaluating tradable property rights for natural resources: The role of strategic entry and exit," Journal of Economic Behavior & Organization, Elsevier, vol. 63(1), pages 158-176, May.
    2. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    3. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Burns, Christopher, 2014. "Measurement Error in the Schaefer Production Model," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170569, Agricultural and Applied Economics Association.

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    Research and Development/Tech Change/Emerging Technologies; Research Methods/ Statistical Methods;

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