A nonparametric multiple choice method within the random utility framework

Many researchers use categorical data analysis to recover individual consumption preferences, but the standard discrete choice models require restrictive assumptions. To improve the flexibility of discrete choice data analysis, we propose a nonparametric multiple choice model that applies the penalized likelihood method within the random utility framework. We show that the deterministic component of the random utility function in the model is a cubic smoothing spline function. The method subsumes the conventional conditional logit model (McFadden, 1973, in: Zarembka, P., (Ed.), Frontiers in Econometrics) as a special case. In this paper, we present the model, describe the estimator, provide the computational algorithm of the model, and demonstrate the model by applying it to nonmarket valuation of recreation sites.