Abstract
The authors report on a procedure for exploiting the information content of rank ordered choice sets to estimate efficiently the parameters of the multinomial logit model formulation of the stochastic utility model of choice behavior. The availability of rank ordered choice set data leads to an “explosion” or decomposition procedure for exploiting such extra information. This “explosion” process involves the decomposition of a ranked choice set into a series of unranked and statistically independent choice sets. In relation to explosion strategies, several heuristics and an analytical procedure for determining the “optimal” explosion depth are discussed in detail. The results of a Monté Carlo study of the small sample properties of the conditional logit estimation procedure (the maximum likelihood estimation procedure used to develop parameter estimates of the multinomial logit model formulation of the stochastic utility model) are reported and interpreted. A college choice empirical application illustrates the procedures developed.
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