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Minimum information management and price‐abundance relationships in a fishery

Author

Listed:
  • Akbar Marvasti
  • Sami Dakhlia
Abstract
We explore the information content of dockside prices and fishing costs in the estimation of stock abundance. Our approach is two‐pronged: we first examine whether the implied biomass, that is, the biomass that is consistent with a simple microeconomic model calibrated with observed prices and costs, offers an approximation of actual stock assessments—both agree over the first 20 years of observation, but diverge over the last five. In a second approach, we use annual data in Vector Autoregressive (VAR), Bayesian VAR (B‐VAR), and Vector Error Correction (VEC) frameworks and add monthly data in a mixed‐frequency data analysis including Mixed‐Frequency Bayesian VAR (MF‐BVAR) and Mixed‐Data Sampling (MIDAS) frameworks for log‐differenced time series. Parameter uncertainties are addressed through Bayesian regression and forecasting methods. We find a statistically significant correlation between biometric estimates and changes in a price‐based indicator that is robust to the inclusion of confounding factors. We conclude that the combination of price data and per‐trip landings, when interpreted with care, can serve as a complementary, but comparatively affordable and timely, source of information for stock assessments. Nous étudions le contenu informationnel des prix du poisson à la criée et des coûts d'exploitation dans le but d'estimer l'abondance des stocks. Notre approche comporte deux volets: dans un premier temps, nous calculons la biomasse implicite, c'est à dire la biomasse qui, compte tenu des prix et des quantités débarquées, est en accord avec un simple modéle d'optimisation des profits. La biomasse implicite et l'estimation biométrique des stocks sont en concordance sur une période de 20 ans, puis divergent sur les cinq dernières années. Dans un deuxième temps, nous utilisons les données annuelles dans le cadre du modèle autorégressif vectoriel (VAR), du VAR bayésien (B‐VAR), et du modèle vectoriel à correction d'erreur (VEC). Nous incorporons ensuite les données mensuelles pour une analyse à fréquences mixtes des séries chronologiques en différence logarithmique, y compris le VAR bayésien à fréquences mixtes (MF‐BVAR) et le modèle de données échantillonnales à fréquences mixtes (MIDAS). Les incertitudes relatives aux paramètres sont adressées par le biais de régressions et prévisions bayésiennes. Nous trouvons une corrélation statistiquement significative entre la mesure d'abondance biométrique et un indice basé sur les prix, corrélation robuste malgré la considération d'une variété de facteurs confondants. Nous concluons qu'une interprétation judicieuse des prix et des quantités débarquées peut produire un complément d'information rapide et peu dispendieux.

Suggested Citation

  • Akbar Marvasti & Sami Dakhlia, 2021. "Minimum information management and price‐abundance relationships in a fishery," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 69(4), pages 491-518, December.
  • Handle: RePEc:bla:canjag:v:69:y:2021:i:4:p:491-518
    DOI: 10.1111/cjag.12299
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    References listed on IDEAS

    as
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