An introduction to estimation with choice-based sample data
Choice-based sampling selects a population conditional on the choices it has made. By allowing the sampling rate to be dependent on choice, certain outcomes can be sampled at higher or lower rates than occurring in the population, and can assure obtaining sufficient observations to conduct an empirical analysis of choice. Samples formed on the basis of choice can produce consistent estimates of the population making a particular choice by using weights. This paper defines the weights, demonstrates their role in estimating the population subgroup making a particular choice, and gives a theorem in the use of weights in maximum likelihood estimation of a choice equation. This theorem greatly enhances the usefulness of choice-based samples in empirical work and, in a special case of the theorem, the conditional logit model, consistent estimates of all parameters except the intercept terms can be obtained from "raw" choice-based data, without weighting.