Author: David T. FrazierPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Weak Identification in Discrete Choice Models
Author: David T. FrazierWeak Identification in Discrete Choice Models
Author: Identification in Discrete Choice Models with Imperfect Information
Author: Cristina GualdaniPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. We leverage the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016) to provide a tractable characterization of the sharp identified set. We develop a procedure to practically construct the sharp identified set when the state of the world is continuous following a sieve approach, and provide sharp bounds on counterfactual outcomes of interest. We use our methodology and data on the 2017 UK general election to estimate a spatial voting model under weak assumptions on agents' information about the returns to voting. Counterfactual exercises quantify the consequences of imperfect information on the well-being of voters and parties.
Semiparametric Identification and Estimation of Discrete Choice Models for Bundles
Author: Fu OuyangApplied Discrete-choice Modelling
Author: David A. HensherPublisher: Taylor & Francis
ISBN: 9780470270783
Category : Decision making
Languages : en
Pages : 468
Book Description
Identification of Semiparametric Discrete Choice Models
Author: T. Scott ThompsonPublisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 53
Book Description
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.
Nonparametric Identification of Discrete Choice Models
Author: John K. DagsvikInterpreting Discrete Choice Models
Author: Garrett GlasgowPublisher: Cambridge University Press
ISBN: 1108877184
Category : Political Science
Languages : en
Pages : 131
Book Description
In discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent variables. Thus, interpreting the magnitude of the effects (the “substantive effects”) of the independent variables on choice behavior requires the use of additional interpretative techniques. Three common techniques for interpretation are described here: first differences, marginal effects and elasticities, and odds ratios. Concepts related to these techniques are also discussed, as well as methods to account for estimation uncertainty. Interpretation of binary logits, ordered logits, multinomial and conditional logits, and mixed discrete choice models such as mixed multinomial logits and random effects logits for panel data are covered in detail. The techniques discussed here are general, and can be applied to other models with discrete dependent variables which are not specifically described here.
Essays 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.