A latent class model for discrete choice analysis: contrasts with mixed logit
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Mixed logit
Discrete choice
Independence
When one estimates discrete choice models, the mixed logit approach is commonly superior to simple conditional logit setups. Mixed logit models not only allow the researcher to implement difficult random components but also overcome the restrictive IIA assumption. Despite these theoretical advantages, the estimation of mixed logit models becomes cumbersome when the model’s complexity increases. Applied works therefore often rely on rather simple empirical specifications because this reduces the computational burden. I introduce the user-written command lslogit, which fits complex mixed logit models using maximum simulated likelihood methods. As lslogit is a d2-ML-evaluator written in Mata, the estimation is rather efficient compared with other routines. It allows the researcher to specify complicated structures of unobserved heterogeneity and to choose from a set of frequently used functional forms for the direct utility function—for example, Box-Cox transformations, which are difficult to estimate in the context of logit models. The particular focus of lslogit is on the estimation of labor supply models in the discrete choice context; therefore, it facilitates several computationally exhausting but standard tasks in this research area. However, the command can be used in many other applications of mixed logit models as well.
Mixed logit
Discrete choice
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When estimating discrete choice models, the mixed logit approach is commonly superior to simple conditional logit setups. Mixed logit models not only allow the researcher to implement difficult random components but also overcome the restrictive IIA assumption. Despite these theoretical advantages, the estimation of mixed logit models becomes cumbersome when the model's complexity increases. Applied works therefore often rely on rather simple empirical specifications as this reduces the computational burden. I introduce the user-written command lslogit which fits complex mixed logit models using maximum simulated likelihood methods. As lslogit is a d2-ML-evaluator written in Mata, the estimation is rather efficient compared to other routines. It allows the researcher to specify complicated structures of unobserved heterogeneity and to choose from a set of frequently used functional forms for the direct utility function---e.g., including Box-Cox transformations which are difficult to estimate in the context of logit models. The particular focus of lslogit is on the estimation of labor supply models in the discrete choice context and therefore it facilitates several computational exhausting but standard tasks in this research area. However, the command can be used in many other applications of mixed logit models as well.
Mixed logit
Discrete choice
Nested logit
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The paper is devoted to the analysis of logit models and their application in the market. A theoretical basis for logit models is determined. Equations for logit probabilities are derived and methods are applied in order to analyze real market situations. The real data set is analyzed to estimate 2 logit models, as well as a probit model. Obtained results are compared with experimentally calculated logit probabilities. Keywords: Decisions; discrete choice model; logit and probit models; simulation; statistical modeling
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Discrete choice
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Discrete choice
Mixed logit
Probit
Ordered probit
Choice set
Nested logit
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Discrete choice
Mixed logit
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Abstract Through creating latent perception hurdles associated with each attribute considered in a stated conjoint experiment, this article describes a model that augments the conventional approach by utilizing the importance ratings provided by respondents prior to the discrete choice stage. The resulting perception hurdle logit (PHL) model has both advantages and disadvantages compared to the conditional logit (CL) and mixed logit models. Although the proposed model may not have the best within‐sample fit, it outperforms the other two models in predicting choices in a hold out sample. In addition, the proposed model is also used to reveal that depending on their own characteristics and the process of the survey, respondents may employ an array of different decision strategies.
Mixed logit
Discrete choice
Conjoint Analysis
Sample (material)
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We compare two options of integrating discrete working time choice of heterogenous households into a general equilibrium model. The first, known from the literature, produces household heterogeneity through a working time preference parameter. We contrast this with a model that directly incorporates a logit discrete-choice approach into a AGE framework. On the grounds of both calibration consistency and adequate accomodation of within-household interaction, we argue that the logit approach is preferable.
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Mixed logit
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This article addresses simultaneously two important features in random utility maximisation (RUM) choice modelling: choice set generation and unobserved taste heterogeneity. It is proposed to develop and to compare definitions and properties of econometric specifications that are based on mixed logit (MXL) and latent class logit (LCL) RUM models in the additional presence of prior compensatory screening decision rules. The latter allow for continuous latent bounds that determine choice alternatives to be or not to be considered for decision making. It is also proposed to evaluate and to test each against the other ones in an application to home-to-work mode choice in the Paris region of France using 2002 data.
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Discrete choice
Choice set
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Econometric model
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ABSTRACT. In estimating a discrete choice model one is actually estimating the parameters of a conditional indirect utility function. I explore the consequences of recognizing that this function is a maximum‐value (frontier) function. I formulate several frontier choice models and, using a pilot empirical study of transportation mode choice, compare the resulting estimates with those of the conventional logit specification. Most strikingly, it appears that the values of time implied by the frontier models are substantially below those of the logit model. This implies that policies designed to improve travel times may be of less value to consumers than is conventionally believed.
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Mixed logit
Discrete choice
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