nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2006‒10‒21
four papers chosen by
Philip Yu
Hong Kong University

  1. Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices By Hiroyuki Kasahara; Katsumi Shimotsu
  2. Evaluating Alternative Representations of the Choice Sets in Models of Labour Supply By Ugo Colombino; R. Aaberge; T. Wennemo
  3. Wage Mobility in Europe. A Comparative Analysis Using restricted Multinomial Logit Regression By Pavlopoulos, Dimitris; Muffels, Ruud; Vermunt, Jeroen-K.
  4. Conjoint analysis and market segmentation By Steiner, Winfried J.; Baumgartner, Bernhard

  1. By: Hiroyuki Kasahara (University of Western Ontario); Katsumi Shimotsu (Queen's University)
    Abstract: In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in applied work. Three elements emerge as the important determinants of identification; the time-dimension of panel data, the number of values the covariates can take, and the heterogeneity of the response of different types to changes in the covariates. For example, in a simple case, a time-dimension of T = 3 is sufficient for identification, provided that the number of values the covariates can take is no smaller than the number of types, and that the changes in the covariates induce sufficiently heterogeneous variations in the choice probabilities across types. Type-specific components are identifiable even when state dependence is present as long as the panel has a moderate time-dimension (T = 6). We also develop a series logit estimator for finite mixture models of dynamic discrete choices and derive its convergence rate.
    Keywords: dynamic discrete choice models; finite mixture; nonparametric identification; panel data; sieve estimator; unobserved heterogeneity
    JEL: C13 C14 C23 C25
    Date: 2006
  2. By: Ugo Colombino; R. Aaberge; T. Wennemo
    Abstract: During the last two decades, the discrete-choice modelling of labour supply decisions has become increasingly popular, starting with Aaberge et al. (1995) and van Soest (1995). Within the literature adopting this approach there are however two potentially important issues t hat so far have not been given the attention they might deserve. A first issue concerns the procedure by which the discrete alternatives are selected to enter the choice set. For example van Soest (1995) chooses (non probabilistically) a set of fixed points identical for every individual. This is by far the most widely adopted method. By contrast, Aaberge et al. (1995) adopt a sampling procedure suggested by McFadden (1978) and also assume that the choice set may differ across the households. A second issue concerns the availability of the alternatives. Most authors assume all the values of hours-of-work within some range [0, H] are equally available. At the other extreme, some authors assume only two or three alternatives (e.g. non-participation, part-time and full-time) are available for everyone. Aaberge et al. (1995) assume instead that not all the hour opportunities are equally available to everyone; they specify a probability density function of opportunities for each individual and the discrete choice set used in the estimation is built by sampling from that individual -specific density function. In this paper we explore by simulation the implications of - the procedure used to build the choice set (fixed alternatives vs sampled alternatives) - accounting or not accounting for a different availability of alternatives. The results of the evaluation performed in this paper show that the way the choice set is represented have little impact on the fitting of observed values, but a more significant and important impact on the out-of-sample prediction performance.
    Keywords: Labour supply, discrete-choice models, quantity constraints, prediction performance
    JEL: C51 C52 H31 J22
    Date: 2006–10
  3. By: Pavlopoulos, Dimitris; Muffels, Ruud; Vermunt, Jeroen-K.
    Abstract: In this paper, we investigate cross-country differences in wage mobility in Europe using the European Community Household Panel. The paper is particularly focused on examining the impact of economic conditions, welfare state regimes and employment regulation on wage mobility. We apply a log-linear approach that is very much similar to a restricted multinomial logit model and much more flexible than the standard probit approach. It appears that regime, economic conditions and employment regulation explain a substantial part of the cross-country variation. The findings also confirm the existence of an inverse U-shape pattern of wage mobility, showing a great deal of low and high-wage persistence in all countries.
    Keywords: wages; wage mobility; wage dynamics; multinomial logit regression; loglinear models; welfare states
    JEL: J3 C19 J31
    Date: 2005–10
  4. By: Steiner, Winfried J.; Baumgartner, Bernhard
    Abstract: Die Marktsegmentierung zählt neben der Neuproduktplanung und Preisgestaltung zu den wesentlichen Einsatzgebieten der Conjoint-Analyse. Neben traditionell eingesetzten zweistufigen Vorgehensweisen, bei denen Conjoint-Analyse und Segmentierung in zwei getrennten Schritten erfolgen, stehen heute mit Methoden wie der Clusterwise Regression oder Mixture-Modellen neuere Entwicklungen, die eine simultane Segmentierung und Präferenzschätzung ermöglichen, zur Verfügung. Der Beitrag gibt einen Überblick über die vorliegenden methodischen Ansätze zur Verknüpfung von Conjoint-Analyse und Marktsegmentierung und zeigt die Vorzüge simultaner Conjointsegmentierungsmethoden gegenüber den in der Unternehmenspraxis noch immer weit verbreiteten zweistufigen Verfahren auf.
    Date: 2006–10–17

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