Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity

Econometrica ◽  
2011 ◽  
Vol 79 (6) ◽  
pp. 1823-1867 ◽  
Keyword(s):  
Discrete Choice ◽  
Unobserved Heterogeneity ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Probability Estimation ◽  
Choice Probability ◽  
Dynamic Discrete Choice

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.

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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