24 research outputs found

    Estimation with the Nested Logit Model: Specifications and Software Particularities

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    Due to its ability to allow and account for similarities betweenpairs of alternatives, the nested logit model is increasingly used in practical applications. However the fact that there are two different specifications of the nested logit model has not received adequate attention. The utility maximization nested logit (UMNL) model and the non-normalized nested logit (NNNL) model have different properties, influencing the estimation results in a different manner. As the NNNL specification is not consistent with random utility theory (RUT), the UMNL form is preferred. This article introduces distinct specifications of the nested logit model and indicates particularities arising from model estimation. Additionally, it demonstrates the performance ofsimulation studies with the nested logit model. In simulation studies with the nested logit model using NNNL software (e. g. PROC MDC in SAS(c) ), it must be pointed out that the simulation of the utility functionĀ“s error terms needs to assume RUT-conformity. But as the NNNL specification is not consistent with RUT, the input parameters cannot be reproduced without imposing restrictions. The effects of using various software packages on the estimation results of a nested logit model are shown on the basis of a simulation study.nested logit model, utility maximization nested logit, non-normalized nested logit, simulation study

    "Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling"

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    The Multinomial Logit (MNL) model is still the only viable option to study nonlinear responsiveness of utility to covariates nonparametrically. This research investigates whether MNL structure of inter-brand competition is a reasonable assumption, so that when the utility function is estimated nonparametrically, the IIA assumption does not bias the result. For this purpose, the authors compare the performance of two comparable nonpara-metric choice models that differ in one aspect: one assumes MNL com-petitive structure and the other infers the pattern of brands' competition nonparametrically from data.

    Nonparametric modeling of buying behavior in fast moving consumer goods markets

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    "From the statistical point of view a nonparametric formulation of a brand choice model (NDE) is a powerful alternative to the logit model. But in the marketing context, researchers in general want to have parameter values to make predictions or to estimate market shares. This leads to a semiparametric model (GAM) formulation with two possible ways of using the results. One is to perform estimation of choice probabilities, but there one is confronted with the same problem as in the nonparametric approach, because no parameters are estimated for the nonparametric part of the model. The second possibility of a semiparametric model formulation overcomes this problem. In addition, with the estimation results a modified parametric model formulation can be estimated. This also gives the possibility to work with the parameter values to estimate market shares or make predictions. Especially for this use of modeling, the underlying data structure should be detected correctly. Therefore, two different estimation algorithms for a GAM were presented and the application of the semiparametric model to a real data set was reported. The estimations were made by the two common algorithms, backfitting and marginal integration, and are compared to each other. An interaction effect in the variable price in the data set was discovered, which leads to the need of additional studies of the data set." (author's abstract

    Preventing tourists from canceling in times of crises

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    Tourism destinations experiencing a crisis are vulnerable to trip cancelations and sudden drops in demand. Little is known about trip cancelations and how to prevent them. Specifically, it is unclear whether the effectiveness of different prevention approaches varies across crises and tourists segments. Using a conjoint design, the present study investigates the comparative stated effectiveness of different prevention approaches in situations where different crises hit a destination. Results indicate that certain prevention actions indeed have the potential to reduce cancelations. The most effective approach is change of accommodation-especially so when combined with an upgrade-followed by information updates and finally the provision of security devices or security staff. The effectiveness of approaches varies across tourists and crises

    A combined approach for segment-specific market basket analysis

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    Market baskets arise from consumers shopping trips and include items from multiple categories that are frequentlychosen interdependently from each other. Explanatory models of multicategory choice behavior explicitly allow for suchcategory purchase dependencies. They typically estimate own and across-category effects of marketing-mix variables onpurchase incidences for a predefined set of product categories. Because of analytical restrictions, however, multicategorychoice models can only handle a small number of categories. Hence, for large retail assortments, the issue emerges of howto determine the composition of shopping baskets with a meaningful selection of categories. Traditionally, this is resolvedby managerial intuition. In this article, we combine multicategory choice models with a data-driven approach for basketselection. The proposed procedure also accounts for customer heterogeneity and thus can serve as a viable tool for designingtarget marketing programs. A data compression step first derives a set of basket prototypes which are representative forclasses of market baskets with internally more distinctive (complementary) cross-category interdependencies and areresponsible for the segmentation of households. In a second step, segment-specific cross-category effects are estimatedfor suitably selected categories using a multivariate logistic modeling framework. In an empirical illustration, significantdifferences in cross-effects and price elasticities can be shown both across segments and compared to the aggregate model
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