| Summary: | This paper deals with the issue of modeling daily catches of fishing boats in the Grand Bank fishing grounds. We have data on catches per species for a number of vessels collected by the European Union in the context of the North 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. In all, our database leads to 23 possible regressors, resulting in a set of $8.4\times 10^6$ possible linear regression models. Prediction of future catches and posterior inference will be based on Bayesian model averaging, using a Markov Chain Monte Carlo Model Composition (MC$^3$) approach. Particular attention is paid to the elicitation of the prior and the prediction of catch for single and aggregated observations. Bayesian model averaging; Grand Bank fisheries; Predictive inference; Prior elicitation
|
|---|