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Bayesian modelling of catch in a north‐west Atlantic fishery

Author

Listed:
  • Carmen Fernández
  • Eduardo Ley
  • Mark F. J. Steel

Abstract

Summary. 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 and the mesh size of the nets) 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‐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 the prediction of catches for single and aggregated ships.

Suggested Citation

  • Carmen Fernández & Eduardo Ley & Mark F. J. Steel, 2002. "Bayesian modelling of catch in a north‐west Atlantic fishery," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 257-280, July.
    • Handle: RePEc:bla:jorssc:v:51:y:2002:i:3:p:257-280
    DOI: 10.1111/1467-9876.00268
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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. Arnoldo Frigessi & Marit Holden & Clare Marshall & Hildegunn Viljugrein & Nils Chr. Stenseth & Lars Holden & Vladimir Ageyev & Nikolay L. Klassovskiy, 2005. "Bayesian Population Dynamics of Interacting Species: Great Gerbils and Fleas in Kazakhstan," Biometrics, The International Biometric Society, vol. 61(1), pages 230-238, March.
    3. 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.
    4. Enrique Moral-Benito, 2010. "Model Averaging in Economics," Working Papers wp2010_1008, CEMFI.
    5. 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.
    6. 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.
    7. 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

    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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