Modeling the spatial behavior of fishers is critical in assessing fishery management policies and has been dominated by discrete choice models (DCM). Motivated widespread availability micro-data on fishing vessel locations, this paper examines complexity associated with scale a DCM locations. Our empirical approach estimates standard at varying resolutions using both simulated data monitoring s...

Outliers in discrete choice response data may result from misclassification and misreporting of the variable behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In presence outliers, standard models produce biased estimates suffer compromised predictive accuracy. Robust statistical are less sensitive to outliers than non-robust models. This paper analys...

Conjoint analysis is family of techniques that originated in psychology and later became popular in market research. The main objective of conjoint analysis is to measure an individual’s or a population’s preferences on a class options that can be described by parameters and their levels. We consider preference data obtained in choice based conjoint analysis studies, where one observes test per...

Discrete choice models used in statistical applications typically interpret an unobservable term as the interaction of unobservable horizontal di¤erentiation and idiosyncratic consumer preferences. An implicit assumption in most such models is that all choices are equally horizontally di¤erentiated from each other. This assumption is problematic in a number of recent studies that use discrete c...

Since the pioneering work by Daniel McFadden in the 1970s and 1980s (McFadden, 1973, 1981, 1982, 1984) discrete (multinomial) response models based on utility maximization have become an important tool of empirical researchers. A key feature of these models is the specification of utilities associated with the alternatives in terms of choice characteristics and individual preferences. Various g...

We propose a multiplicative speci cation of a discrete choice model that renders choice probabilities independent of the scale of the utility. The scale can thus be random with unspeci ed distribution. The model mostly outperforms the classical additive formulation over a range of stated choice data sets. In some cases, the improvement in likelihood is greater than that obtained from adding obs...

We propose a new estimator for dynamic programming discrete choice models. Our estimation method combines the Dynamic Programming algorithm with a Bayesian Markov Chain Monte Carlo algorithm into one single Markov Chain algorithm that solves the dynamic programming problem and estimates the parameters at the same time. Our key innovation is that during each solution-estimation iteration both th...

In this research, we provide a new method to estimate discrete choice models with unobserved heterogeneity that can be used with either cross-sectional or panel data. The method imposes nonparametric assumptions on the systematic subutility functions and on the distributions of the unobservable random vectors and the heterogeneity parameter. The estimators are computationally feasible and stron...