Genetic Algorithms for Econometric Optimization

This paper discusses a tool for optimization of econometric models based on genetic algorithms. Due to the increasing
popularity of semi–parametric estimators, researchers often have to optimize non–differentiable, non–smooth criterion
functions that cannot be solved with conventional gradient methods. Genetic algorithms constitute an alternative
opportunity for optimization. So far, however, applications are not wide–spread, as there is no software readily available
yet. This paper overcomes this limitation by describing a genetic algorithm implemented in STATA that can easily
be dynamically linked to any criterion function to be optimized. We describe the algorithm, provide two example
applications, and provide a step–by–step guide on how to use the software.

Czarnitzki, Dirk and Thorsten Doherr (2026), Genetic Algorithms for Econometric Optimization, Letters in Economic Research 2(3)