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Assimilation of Time Series Data into a Dynamic Bioeconomic Fisheries Model: An Application to the North East Arctic Cod Stock


  • Al-Amin Ussif


  • Leif Sandal


  • Stein Steinshamn



This paper combines the elegant technique of Data Assimilation and a Monte Carlo procedure to analyze time series data for the North East Arctic Cod stock (NEACs). A simple nonlinear dynamic resource model is calibrated to time series data using the variational adjoint parameter estimation method and the Monte Carlo technique. By exploring the efficient features of the variational adjoint technique coupled with the Monte Carlo method, optimal or best parameter estimates with their error statistics are obtained. Thereafter, the weak constraint formulation resulting in a stochastic ordinary differential equation (SODE) is used to find an improved estimate of the dynamical variable, i.e. the stock. Empirical results show that the average fishing mortality imposed on the NEACs is about 16% more than the intrinsic growth rate of the biological species. Copyright Springer 2005

Suggested Citation

  • Al-Amin Ussif & Leif Sandal & Stein Steinshamn, 2005. "Assimilation of Time Series Data into a Dynamic Bioeconomic Fisheries Model: An Application to the North East Arctic Cod Stock," Journal of Bioeconomics, Springer, vol. 7(2), pages 179-195, January.
  • Handle: RePEc:kap:jbioec:v:7:y:2005:i:2:p:179-195
    DOI: 10.1007/s10818-004-4143-6

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    References listed on IDEAS

    1. Ragnar Arnason & Leif K. Sandal & Stein Ivar Steinshamn & Niels Vestergaard, 2004. "Optimal Feedback Controls: Comparative Evaluation of the Cod Fisheries in Denmark, Iceland, and Norway," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 531-542.
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    More about this item


    dynamic resource model; Inverse methods; Monte Carlo; variational adjoint parameter estimation; weak constraint; C15; Q22;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • Q22 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Fishery


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