When valuing water resources using multiple site recreation data, it is typical to observe many corner solutions. We present as a benchmark a theoretical model of recreation choice when corners are present, and assess the degree to which several empirical specifications match the benchmark. As an illustration, we estimate and compare the repeated nested logit, a random parameters repeated neste...

As shown by Guimaraes, Figueiredo and Woodward (2003), a particular class of conditional logit models yield identical parameter estimates to a Poisson count data model. In Schmidheiny and Brülhart (2011), we have pointed out that the conditional logit model and the Poisson model can be seen as polar cases of a continuum of intermediate cases which emerge from a random utility nested logit model...

This talk considers the special case of binomial data where repeated measurements are available for identical covariate patterns. Our method is based on various Bayesian approaches for estimating binary logit models. To carry out MCMC sampling with data augmentation, logit models are rewritten as random utility models (RUM) or difference RUM (dRUM). Following earlier papers (Frühwirth-Schnatter...

Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into fe...

The multinomial probit (MNP) model is a primary application for combining simulation with estimation. Indeed, McFadden (1989) featured the MNP model in his seminal paper. As random utility model, the MNP model offers a highly desirable flexibility in substitution among alternatives that its chief rival, the multinomial logit model, fails to possess. The unrestricted character of the variance ma...

The simulation variance in the estimation of mixed logit parameters is found, in our application, to be lower with 100 Halton draws than with 1000 random draws. This finding confirms Bhat’s (1999a) results and implies significant reduction in run times for mixed logit estimation. Further investigation is needed to assure that the result is not quixotic or masking other issues.

The simulation variance in the estimation of mixed logit parameters is found, in our application, to be lower with 100 Halton draws than with 1000 random draws. This finding confirms Bhat’s (1999a) results and implies significant reduction in run times for mixed logit estimation. Further investigation is needed to assure that the result is not quixotic or masking other issues.

We consider assortment optimization problems under the multinomial logit model, where the parameters of the choice model are random. The randomness in the choice model parameters is motivated by the fact that there are multiple customer segments, each with different preferences for the products, and the segment of each customer is unknown to the firm when the customer makes a purchase. This cho...

Mixed logit is a highly flexible model that can approximate any random utility model. In this paper, two kinds of mixed multinomial logit models were considered. The main aim was to introduce a locally D-optimal criterion to obtain an optimal combination of the levels of attributes for producing alternatives and an optimal combination of alternatives in choice sets. Thus, a design including cho...

In 2011, Lake Erie experienced a record-setting harmful algal bloom (HAB), posing significant risks to ecosystem services, including its $1.5 billion sport fishing industry. Using a mail survey of 3,000 Ohio recreational anglers and a choice experiment, this article provides the first empirical evidence in the US to link HABs to damages to Great Lakes recreational anglers. We account for the he...