Unobserved Heterogeneity in Dynamic Discrete Choice Models

1993 ◽  
Vol 25 (4) ◽  
pp. 495-519 ◽  
Author(s):  
S Reader
Keyword(s):  
Discrete Choice ◽  
Simulation Analysis ◽  
Choice Model ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Estimation Procedure ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Dynamic Discrete Choice ◽  
Cross Sectional

Monte Carlo simulation methods are used to confirm the identifiability of discrete choice models in which unobserved heterogeneity is specified as a random effect and modelled using the nonparametric mass-points approach. This simulation analysis is also used to examine alternative strategies for the estimation of such models by using a quasi-Newton maximum-likelihood estimation procedure, given the apparent sensitivity of model identification to choice of starting values. A mass-point model approach is then applied to a dataset of repeated choice involving household shopping trips between three types of retail centre, and the results from this approach are compared with those obtained from a conventional cross-sectional multinomial logit choice model as well as to results from a model in which a parametric distribution (the Dirichlet) is used to model the unobserved heterogeneity.

Modeling Store Choices with Cross-Sectional and Pooled Cross-Sectional Data: A Comparison

1995 ◽  
Vol 27 (8) ◽  
pp. 1303-1315 ◽  
Author(s):  
J-C Thill
Keyword(s):  
Market Share ◽  
Discrete Choice ◽  
Marginal Utility ◽  
Choice Model ◽  
Practical Implication ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Destination Choice ◽  
Cross Sectional ◽  
Data Set

Contrary to many other types of spatial decisions, shopping destination choice behavior is highly repetitive. For the practitioner looking for good predictors of store patronage, for reliable marginal utility estimates and reliable market share predictions, a central concern is with the type of data best suited to the research question, given the existing logistic and financial constraints. Different approaches can be recognized in the literature in which conventional discrete choice models are applied to shopping destination choice problems. In this paper, two of the most common practices are assessed and compared. First, the choice model is estimated with all choices of a relevant destination observed during a certain period of time (pooled cross-sectional data). The alternative approach consists in an estimation with the choice of the destination where the majority of purchases takes place (cross-sectional data). In the particular data set employed here, no evidence is found to support the idea that a multinomial logit model estimated with cross-sectional data does not perform as well as a model estimated with pooled cross-sectional data. Both models are found to be similar in their ability to identity the main predictors of store choice. Models developed on either data sets have marginal utility estimates that exhibit no statistically significant differences. Finally, market share predictions derived from both models are not statistically different. It appears, therefore, that there is no need to collect repeated patronage data over an extended period of time. The practitioner who wishes to use a conventional discrete choice model may avoid spending much time and money by gathering limited data on regular patronage patterns. In addition to this practical implication, the conclusions suggest that regular shopping destinations are chosen in accordance with the same behavioral motives as ancillary destinations are.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

scholarly journals Solving dynamic discrete choice models using smoothing and sieve methods

2020 ◽  
Author(s):  
Dennis Kristensen ◽  
Patrick K. Mogensen ◽  
Jong Myun Moon ◽  
Bertel Schjerning
Keyword(s):  
Discrete Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Dynamic Discrete Choice ◽  
Sieve Methods
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