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Bayesian Modelling of Catch in a Northwest Atlantic Fishery

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Author Info

  • 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.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0110003.

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Date of creation: 06 Oct 2001
Date of revision: 18 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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Web page: http://128.118.178.162

Related research

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

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References

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  1. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  2. Min, C.K. & Zellner, A., 1992. ""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates"," Papers 90-92-23, California Irvine - School of Social Sciences.
  3. Carmen Fernández & Eduardo Ley & Mark F. J. Steel, . "Benchmark priors for Bayesian Model averaging," Working Papers 98-06, FEDEA.
  4. 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.
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Cited by:
  1. Abidoye, Babatunde & Herriges, Joseph A., 2010. "Model Uncertainty in Characterizing Recreation Demand," Staff General Research Papers 32028, Iowa State University, Department of Economics.
  2. Enrique Moral-Benito, 2011. "Model averaging in economics," Banco de Espa�a Working Papers 1123, Banco de Espa�a.
  3. Irwin Guttman & Daniel Peña & M Dolores Redondas, 2003. "A Bayesian Approach for Predicting with Polynomial Regresión of Unknown Degree," Statistics and Econometrics Working Papers ws032104, Universidad Carlos III, Departamento de Estadística y Econometría.
  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.

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