Semiparametric Discrete Choice Models for Bundles
Author | : Fu Ouyang |
Publisher | : |
Total Pages | : |
Release | : 2020 |
Genre | : |
ISBN | : |
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Author | : Fu Ouyang |
Publisher | : |
Total Pages | : |
Release | : 2020 |
Genre | : |
ISBN | : |
Author | : Fu Ouyang |
Publisher | : |
Total Pages | : |
Release | : 2020 |
Genre | : |
ISBN | : 9780868316727 |
Author | : T. Scott Thompson |
Publisher | : |
Total Pages | : 53 |
Release | : 1989 |
Genre | : Econometric models |
ISBN | : |
Author | : Arthur Lewbel |
Publisher | : |
Total Pages | : |
Release | : 2021 |
Genre | : |
ISBN | : |
Author | : T. Scott Thompson |
Publisher | : |
Total Pages | : 22 |
Release | : 1991 |
Genre | : |
ISBN | : |
Author | : Khai Chiong |
Publisher | : |
Total Pages | : 19 |
Release | : 2017 |
Genre | : |
ISBN | : |
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.
Author | : Wei Song |
Publisher | : |
Total Pages | : 162 |
Release | : 2017 |
Genre | : |
ISBN | : |
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.
Author | : Eric Mbakop |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : |
ISBN | : |
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 | : Christopher Taber |
Publisher | : |
Total Pages | : 242 |
Release | : 1995 |
Genre | : Decision making |
ISBN | : |
Author | : Kazumitsu Nawata |
Publisher | : |
Total Pages | : 26 |
Release | : 1989 |
Genre | : |
ISBN | : |