Author: Fu OuyangPublisher:
ISBN: 9780868316727
Category :
Languages : en
Pages :
Book Description
Semiparametric Identification and Estimation of Discrete Choice Models for Bundles
Author: Fu OuyangSemiparametric Discrete Choice Models for Bundles
Author: Fu OuyangIdentification of Semiparametric Discrete Choice Models
Author: T. Scott ThompsonPublisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 53
Book Description
Semiparametric Identiđ“ŹŠtion and Estimation of Multinomial Discrete Choice Models Using Error Symmetry
Author: Arthur LewbelEssays on Discrete Choice Models
Author: Wei SongPublisher:
ISBN:
Category :
Languages : en
Pages : 162
Book Description
This dissertation focuses on the identification and estimation of discrete choice models. In practice, if the error term is independent of the covariates and follows some known distribu- tion, the discrete choice model is usually estimated using some parametric estimator, such as Probit and Logit. However, when the distribution of the error is unknown, misspecification would in general cause the estimators inconsistent even if the independence between the covariates and the error still holds. The two chapters relax the assumptions on the error distribution in the discrete choice models and propose semiparametric estimators.
Essays on Estimation of Discrete Choice Models with Endogeneity
Author: Nese YildizCounterfactual Estimation in Semiparametric Discrete-Choice Models
Author: Khai ChiongPublisher:
ISBN:
Category :
Languages : en
Pages : 19
Book Description
We show how to construct bounds on counterfactual choice probabilities in semiparametric discrete-choice models. Our procedure is based on cyclic monotonicity, a convex-analytic property of the random utility discrete-choice model. These bounds are useful for typical counterfactual exercises in aggregate discrete-choice demand models. In our semiparametric approach, we do not specify the parametric distribution for the utility shocks, thus accommodating a wide variety of substitution patterns among alternatives. Computation of the counterfactual bounds is a tractable linear programming problem. We illustrate our approach in a series of Monte Carlo simulations and an empirical application using scanner data.
Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices
Author: Hiroyuki KasaharaPublisher:
ISBN: 9780771428081
Category : Mixture distributions (Probability theory)
Languages : en
Pages : 45
Book Description
In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an im- portant 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 {u2265} 6). We also develop a series logit estimator for finite mixture models of dynamic discrete choices and derive its convergence rate.
Identification in Some Discrete Choice Models
Author: Eric MbakopPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This paper develops a new computational method that generates all the conditional moment inequalities that characterize the identified set of the parametric components of several semi- parametric panel data models of discrete choice. I consider very flexible models that only impose weak distributional restrictions on the joint distribution of the covariates, fixed effects and shocks. By exploiting the discreteness and convexity of the problem, I show that the identified set of the parametric component of the model can be characterized from the extreme points of a polytope which I describe explicitly. A direct implication of this observation is that finding all the inequalities that characterize the sharp identified set can be viewed as a purely computational problem, and any algorithm that can retrieve all the extreme points of our polytopes recovers all the inequality restrictions that characterize the identified set. The determination of all the extreme points of a polytope is a computational difficult task, and I exploit the particular structure the polytopes that occur in discrete choice models to propose an algorithm that works well for problems of moderate size. The algorithm is used to re-derive many known results: The algorithm can, for instance, recover all the conditional moment inequalities that were found in Manski 1987, Pakes and Porter 2021 and Khan, Ponomareva, and Tamer 2021. I also use the algorithm to generate some new conditional moment inequalities under alternative distributional assumptions, as well to generate new inequalities in some cases that were left open in Pakes and Porter 2021 and Khan, Ponomareva, and Tamer 2021.
Author: T. Scott Thompson