Optimization Heuristics in Economics and Statistics
 
Prof. Manfred Gilli
University of Geneva, Switzerland.

Abstract:

Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods. One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying. A different approach consists in applying optimization heuristics such as local search methods or evolutionary algorithms, i.e.~Simulated Annealing, Threshold Accepting, Neural Networks, Genetic Algorithms, Tabu Search, hybrid methods and many others, which have been developed over the last two decades. Although the use of these methods became more standard in several fields of sciences, their use in estimation and modelling in statistics appears to be still limited.

We present a brief introduction to the computational complexity of problems encountered in the fields of statistical modelling and econometrics and comment the difference between the standard and heuristic optimization paradigm. The main optimization heuristics will be reviewed and also some elements for classification will be provided.

Given the growing availability of optimization heuristics, it is expected that their use will become more frequent in statistics in the near future.

Keywords:

Optimization heuristics, Modelling