Estimating a Destination‐Choice Model from a Choice‐based Sample with Limited Information

In most applications of multinomial logit and other probabilistic discrete‐choice models, the estimation data set is either a simple random sample of the population of interest or an exogenously stratified sample. Often, however, it is cheaper and easier to sample individuals while they are carrying out the chosen activity of concern. This produces a choice‐based sample, which presents important problems of estimation and inference. This paper is concerned with estimation of destination‐choice models from choice‐based samples when neither the aggregate market shares of alternatives nor the probability distribution of explanatory variables in the population is known. The method of Cosslett (1981) for estimating multinomial logit models from such data is summarized, and the limitations on information about choice behavior that can be recovered from the sample are explained. An empirical model of pharmacy choice in the Namur, Belgium, area is presented. It is shown that useful and important information about destination‐choice behavior can be obtained from a choice‐based sample, even without knowledge of aggregate market shares and the probability distribution of explanatory variables. 1991 The Ohio State University