The authors thank the collaborative research partner for sharing insights and providing the opportunity to conduct this study. The authors acknowledge Bas Donkers, Marnik Dekimpe, Peter Leeflang, and Koen Pauwels for their helpful comments on previous versions of this article. Furthermore, they are indebted to the three anonymous JMR reviewers for their constructive comments.

We introduce a multi-level smooth transition model for a panel of time series, which can be used to examine the presence of common nonlinear business cycle features across many variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which allows for unrestricted nonlinearity. We introduce a second-stage model linking the parameters that determine the timing of the switches between business cycle regimes to observable explanatory variables, thereby allowing for lead-lag relationships across panel members. We discuss representation, estimation by concentrated simulated maximum likelihood and inference. We illustrate our model using quarterly industrial production in 19 US manufacturing sectors, and document that there are subtle differences across sectors in leads and lags for switches between business cycle recessions and expansions. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper we consider the situation where one wants to study the preferences of individuals over a discrete choice set through a survey. In the classical setup respondents are asked to select their most preferred option out of a (selected) set of alternatives. It is well known that, in theory, more information can be obtained if respondents are asked to rank the set of alternatives instead. In statistical terms, the preferences can then be estimated more efficiently. However, when individuals are unable to perform (part of) this ranking task, using the complete ranking may lead to a substantial bias in parameter estimates. In practice, one usually opts to only use a part of the reported ranking.In this paper we introduce a latent-class rank-ordered logit model in which we use latent segments to endogenously identify the ranking capabilities of individuals. Each segment corresponds to a different assumption on the ranking capability. Using simulations and an empirical application, we show that using this model for parameter estimation results in a clear efficiency gain over a multinomial logit model in case some individuals are able to rank. At the same time it does not suffer from biases due to ranking inabilities of some of the respondents.JEL Classification: C25, C52

Being able to accurately predict what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large assortments maintained by online retailers, scalability of the prediction method is just as important as its accuracy. We study two classes of models that use such data to predict what a customer will buy next: A novel approach that uses latent Dirichlet allocation (LDA), and mixtures of Dirichlet-Multinomials (MDM). A key benefit of a model-based approach is the potential to accommodate observed customer heterogeneity through the inclusion of predictor variables. We show that LDA can be extended in this direction while retaining its scalability. We apply the models to purchase data from an online retailer and contrast their predictive performance with that of a collaborative filter and a discrete choice model. Both LDA and MDM outperform the other methods. Moreover, LDA attains performance similar to that of MDM while being far more scalable, rendering it a promising approach to purchase prediction in large assortments.

This article examines the global spill-over of foreign product introductions and takeoffs on a focal country's time-to-takeoff, using a novel data set of penetration data for 8 high tech products across 55 countries. It shows how foreign clout, the susceptibility to foreign influences, and intercountry distances affect global spill-over patterns. The authors find that foreign takeoffs, but not foreign introductions, accelerate a focal country's time-to-takeoff. The larger the country, the higher its economic wealth, and the more it exports, the more clout it has in the global spill-over process. In contrast, the poorer the country, the more tourists it receives and the higher its population density, the more susceptible it is to global spill-over effects. Cross-country spill-over effects are stronger the closer the countries are to one another, both geographically and economically, but not necessarily in terms of culture. The model the authors develop also quantifies the spill-over between each country-pair, allowing it to be asymmetric. Modeling Global Spill-Over of New Product Takeoff ABSTRACTThis article examines the global spill-over of foreign product introductions and takeoffs on a focal country's time-to-takeoff, using a novel data set of penetration data for 8 high tech products across 55 countries. It shows how foreign clout, the susceptibility to foreign influences, and inter-country distances affect global spill-over patterns. The authors find that foreign takeoffs, but not foreign introductions, accelerate a focal country's time-to-takeoff. The larger the country, the higher its economic wealth, and the more it exports, the more clout it has in the global spillover process. In contrast, the poorer the country, the more tourists it receives and the higher its population density, the more susceptible it is to global spill-over effects. Cross-country spill-over effects are stronger the closer the countries are to one another, both geographically and economically, but not necessarily in terms of culture. The model the authors develop also quantifies the spill-over between each country-pair, allowing it to be asymmetric. Keywords