We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved sets. recover the joint distribution sets preferences from panel dataset on choices. assume that either latent are sparse or is sufficiently long. Sparsity requires number possible to be relatively small. It satisfied, instance, when nested, they form partition. Our procedure comp...

In contrast to the classical discrete choice experiment, the respondent in a rank-order discrete choice experiment, is asked to rank a number of alternatives instead of the preferred one. In this paper, we study the information matrix of a rank order nested multinomial logit model (RO.NMNL) and introduce local D-optimality criterion, then we obtain Locally D-optimal design for RO.NMNL models in...

This paper provides numerically trivial estimators for short panels of either binary choices or of linear models that suffer from confounded, missing not at random sample selection. The estimators allow for Þxed effects, endogenous regressors, weakly exogenous regressors, and heterokedastic errors with unknown distribution. The estimators, which converge at rate root n, are based on variants of...