Sampling of alternatives in Multivariate Extreme Value (MEV) models

We propose a methodology to achieve consistency, asymptotic normality and efficiency, while sampling alternatives in Multivariate Extreme Value (MEV) models, extending a previous result for Logit. We illustrate the methodology and study the finite sample properties of the estimators using Monte Carlo experimentation and real data on residential location choice from Lisbon, Portugal. Experiments show that the proposed methodology is practical, that it outperforms the uncorrected model, and that it yields acceptable results, even for relatively small samples of alternatives. The paper finishes with a synthesis and an analysis of the impact, limitations and potential extensions of this research.

► We analyze the problem of sampling of alternatives in discrete choice models. ► Develop a novel method for Multivariate Extreme Value (MEV) models. ► Derive the asymptotic distribution of its estimators and show relative efficiency. ► Use Monte Carlo and real data to illustrate and to study small sample properties.