Estimation Techniques for MEV Models with Sampling of Alternatives
Ricardo Hurtubia, Gunnar Flötteröd, Michel Bierlaire · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2010
Estimation of MEV models with large choice sets requires sampling of alternatives, which might be a dicult task due to the correlated-structure of the error terms. Standard sampling techniques like the ones traditionally used for Multinomial Logit models can not be di-rectly applied in the estimation of more complex MEV models. State of the art estimators for MEV models with sampling of alternatives either require knowledge of the full choice set or produce biased es-timates for small sample sizes. This paper proposes two estimation techniques for MEV models with sampling of alternatives. The rst technique is based on bootstrapping and allows to reduce the bias for existing estimators. The second technique introduces a new estimator, based on importance sampling, which generates unbiased parameter estimates for small sample sizes. 1