This project develops a model of spatiotemporal fishing behavior that incorporates the dynamic and general equilibrium elements of catch-share fisheries. We construct an estimation strategy that is able to recover structural behavioral parameters through a nested fixed-point maximum likelihood procedure. The modeling approach is illustrated through a Monte Carlo analysis. We demonstrate its importance for predicting out-of-sample counterfactual policies.
The corresponding paper associated with this project is:
Reimer, M.N., J.K. Abbott, and A.C. Haynie (2020) "Structural Behavioral Models for Rights-Based Fisheries"
| Script Name | Description |
|---|---|
| parent_script.R | A guide for how to generate data and estimate the RERUM model. |
| monte_carlo_data.m | Generates data and estimates from the data generating process. Data are either generated with a random draw from the parameter space or using a pre-determined set of parameters. |
| monte_carlo_analysis.m | Analyzes the Monte Carlo data and evaluates estimation and in-sample performance. |
| policy_simulations.m | Generates policy simulations for bycatch TAC reductions and hot-spot closures. |
- Optimization Toolbox
- Parallel Computing Toolbox (Monte Carlo draws are computed in parallel)
- Statistics and Machine Learning Toolbox (only for the evrnd() function)