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Title: Statistical and computational tradeoff in econometric models building by genetic algorithms Authors:  Manuel Rizzo - Sapienza University of Rome (Italy) [presenting]
Francesco Battaglia - University La Sapienza, Rome (Italy)
Abstract: When a Genetic Algorithm (GA), or in general a stochastic algorithm, is employed in a statistical problem, the result is affected by both the variability due to sampling error, due to the fact that only a sample is observed, and the variability due to the stochastic nature of the algorithm. Such issues can be analyzed by understanding the trade-off between statistical accuracy and computational efforts. We focus on statistical estimation problems for which the variability of the GA estimates can be decomposed in the two sources of variability by means of cost functions, related to both data acquisition and runtime of the algorithm. Simulation studies will be presented to discuss the statistical and computational tradeoff question.