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Machine learning for resource economics : a review of modern computational statistics with empirical applications in fisheries management

Potter, Ryan Jeffrey
Master thesis
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URI
https://hdl.handle.net/11250/2644607
Date
2019
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  • Master Thesis [3363]
Abstract
Machine learning increasingly permeates our everyday lives, from artificial intelligence

suggesting how we complete a text message to big data selecting creepily relevant ads to show

us as we browse the web. While science and technology researchers have pushed these

methods forward and private companies have embraced their power in significant changes to

their processes, the field of economics has largely watched them go by. Despite the credibility

revolution and increased focus on estimating causal effects, the econometric techniques in use

today are largely identical to the ones used three decades ago.

This thesis contributes to the growing field of literature at the intercept of machine learning

and economics by exploring whether modern computational statistics methods can provide

practical value to resource economists. I answer the following research question:

Can integrating machine learning methods into econometric models improve upon traditional

methods and add value in solving resource economics problems?

To answer this question, I review the machine learning literature on causal analysis to find that

machine learning methods solve certain types of problems in unique ways that traditional

methods cannot. To test the benefit of these new methods in a resource economics setting, I

apply machine learning to a fisheries problem based on the Costello, Gaines, & Lynham

(2008a) article, Can Catch Shares Prevent Fisheries Collapse?, and analyse performance in

a first-stage estimation task for propensity score matching.

The results show machine learning can improve performance for prediction-based

econometrics tasks under certain conditions. Shrinkage-based methods like Lasso regression

proved to substantially improve model fit for datasets with moderate variance, while

performing in-line with traditional methods when this condition didn’t hold. While more

flexible methods like Random Forest performed extremely well fitting the data, they captured

significant levels of noise by overfitting, challenging the external validity of their predictions.

Machine learning identified and modelled valid selection bias that traditional methods could

not – demonstrating value in solving practical resource economics problems. The impact of

first-stage overfitting on the final causal model was unclear and presents an important area for

further research, but the overall findings support the application of machine learning methods

for robustness analysis on prediction tasks in resource economics.

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