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Bayesian Modelling of Catch in a Northwest Atlantic Fishery

Author

Listed:
  • Carmen Fernandez

    (University of Saint Andrews)

  • Eduardo Ley

    (IMF Institute)

  • Mark Steel

    (University of Kent at Canterbury)

Abstract

We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used, the mesh size of the nets, etc.), are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log of catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to prediction of catch for single and aggregated ships.

Suggested Citation

  • Carmen Fernandez & Eduardo Ley & Mark Steel, 2001. "Bayesian Modelling of Catch in a Northwest Atlantic Fishery," Econometrics 0110003, EconWPA, revised 23 Nov 2001.
  • Handle: RePEc:wpa:wuwpem:0110003
    Note: Type of Document - Tex; prepared on MacOS, TeXtures; to print on any printer; figures: included. Revised for JRSS-C- (Applied Statistics). Data and f77 code available from:
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    References listed on IDEAS

    as
    1. Eva Ferreira & Fernando Tusell, 1996. "Un modelo aditivo semiparamétrico para estimación de capturas: el caso de las pesquerías de Terranova," Investigaciones Economicas, Fundación SEPI, vol. 20(1), pages 143-157, January.
    2. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    4. James Kirkley & Dale Squires & Ivar Strand, 1998. "Characterizing Managerial Skill and Technical Efficiency in a Fishery," Journal of Productivity Analysis, Springer, vol. 9(2), pages 145-160, March.
    5. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
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    Citations

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    Cited by:

    1. Babatunde Abidoye & Joseph Herriges, 2012. "Model Uncertainty in Characterizing Recreation Demand," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 53(2), pages 251-277, October.
    2. Durlauf, Steven N. & Navarro, Salvador & Rivers, David A., 2016. "Model uncertainty and the effect of shall-issue right-to-carry laws on crime," European Economic Review, Elsevier, vol. 81(C), pages 32-67.
    3. Enrique Moral-Benito, 2010. "Model Averaging in Economics," Working Papers wp2010_1008, CEMFI.
    4. Theo Eicher & Jeff Begun, 2008. "In Search of a Sulphur Dioxide Environmental Kuznets Curve: A Bayesian Model Averaging Approach," Working Papers UWEC-2007-19-P, University of Washington, Department of Economics.
    5. Guttman, Irwin & Peña, Daniel & Redondas, María Dolores, 2003. "A bayesian approach for predicting with polynomial regresión of unknown degree," DES - Working Papers. Statistics and Econometrics. WS ws032104, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Yiyun Shou & Michael Smithson, 2015. "Evaluating Predictors of Dispersion: A Comparison of Dominance Analysis and Bayesian Model Averaging," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 236-256, March.

    More about this item

    Keywords

    Bayesian Model Averaging; Choice of Regressors; Bayesian model averaging; Categorical variables; Grand Bank fishery; Modelling Fish Catch; Predictive inference; Probit model;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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