Download or read book Semiparametric Estimation Joint Discrete continuous Choice Models written by Keith Allan Heyen and published by . This book was released on 1992 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation of Joint Dicrete continuous Choice Models written by Keith Allan Heyen and published by . This book was released on 1992 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation of Binary Discrete Choice Models written by Margarida Genius and published by . This book was released on 1990 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Semiparametric Estimation of Multinomial Discrete Choice Models written by and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter I propose a semiparametric estimator that allows for a flexible form of heteroskedasticity for multinomial discrete choice (MDC) models. Despite being semiparametric, the rate of convergence of the smoothed maximum score (SMS) estimator is not affected by the number of alternative choices. I show the strong consistency and asymptotic normality of the proposed estimator. The rate of convergence of the SMS estimator for MDC models can be made arbitrarily close to the inverse of the square root of the sample size, which is the same as the rate of convergence of Horowitz's (1992) SMS estimator for the binary response model. Monte Carlo experiments provide evidence that the proposed estimator has a smaller mean squared error than both the conditional logit estimator and the maximum score estimator when heteroskedasticity exists. I apply the SMS estimator to study the college decisions of high school graduates using a subset of Chilean data from 2011. The estimation results of the SMS estimator differ significantly from the results of the conditional logit estimator, which suggests possible misspecification of parametric models and the usefulness of considering the SMS estimator as an alternative for estimating MDC models. Many MDC applications include potentially endogenous regressors. To allow for endogeneity, in the second chapter I propose a two-stage instrumental variables estimator where the endogenous variable is replaced by a linear estimate, and then the preference parameters in the MDC equation are estimated by the SMS estimator described in the first chapter. In neither stage do I specify the distribution of the error terms, so this two-stage estimation method is semiparametric. This estimator is a generalization of the estimator proposed by Fox (2007). Fox suggests applying the maximum score estimator in the second stage of estimation. This chapter is the first to derive the statistical properties of an estimator allowing for endogeneity in this semiparametric setting. The two-stage instrument variables estimator is consistent when the linear function of instrument variables and other covariates can rank order the choice probabilities. The second chapter also provides results of some Monte Carlo experiments.

Download or read book Semiparametric Estimation of Binary Choice Models Based on Medians of the Grouped Data written by Kazumitsu Nawata and published by . This book was released on 1989 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation of Discrete Choice Models written by Trueman Scott Thompson and published by . This book was released on 1989 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models written by Andriy Norets and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a tractable semiparametric estimation method for dynamic discrete choice models. The distribution of additive utility shocks is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions and the flexibility of the location-scale mixtures. We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible jump algorithm. For binary dynamic choice model, our approach delivers estimation results that are consistent with the previous literature. We also apply the proposed method to multinomial choice models, for which previous literature does not provide tractable estimation methods in general settings without distributional assumptions on the utility shocks. In our simulation experiments, we show that the standard dynamic logit model can deliver misleading results, especially about counterfactuals, when the shocks are not extreme value distributed. Our semiparametric approach delivers reliable inference in these settings. We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and posterior concentration of the set identified utility parameters and the distribution of shocks in the model.

Download or read book A Note on Semiparametric Estimation of Finite Mixtures of Discrete Choice Models with Application to Game Theoretic Models written by Patrick Bajari and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We view a game abstractly as a semiparametric mixture distribution and study the semiparametric efficiency bound of this model. Our results suggest that a key issue for inference is the number of equilibria compared to the number of outcomes. If the number of equilibria is sufficiently large compared to the number of outcomes, root-n consistent estimation of the model will not be possible. We also provide a simple estimator in the case when the efficiency bound is strictly above zero.

Download or read book Nonparametric and Semiparametric Estimation of Additive Models with Both Discrete Continuous Variables Under Dependence written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Identi tion and Estimation of Multinomial Discrete Choice Models Using Error Symmetry written by Arthur Lewbel and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation and Efficiency Bounds of Binary Choice Models when the Models Contain One Continuous Variable written by Kazumitsu Nawata and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Counterfactual Estimation in Semiparametric Discrete Choice Models written by Khai Chiong and published by . This book was released on 2017 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Essays on Estimation of Discrete Choice Models with Endogeneity written by Nese Yildiz and published by . This book was released on 2005 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Identification and Estimation of Discrete Choice Models for Bundles written by Fu Ouyang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonparametric and Semiparametric Estimation of Additive Models with Both Discrete and Continuous Variables Under Dependence written by Christine Camlong-Viot and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Identification in Some Discrete Choice Models written by Eric Mbakop and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Flexible Multiple Discrete continuous Choice Structures and Mixed Modeling written by Sebastian Astroza and published by . This book was released on 2018 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Multiple discrete-continuous (MDC) choice situations, consumers choose one or more alternatives from a set of alternatives jointly with the amount of the chosen alternative to consume. The MDC model that has dominated the recent literature is based on an utility maximization framework. In the utility functional form, each alternative is assumed to have a baseline preference (marginal utility at the point of zero consumption). Stochasticity is usually introduced in these baseline preferences as a kernel stochastic error term to acknowledge the presence of unobserved factors that may impact the utility of each alternative. Researchers have also introduced random structures for the coefficients on the exogenous variables that allow heterogeneity (across individuals) in the sensitivity to exogenous variables. At the same time as there is more emphasis on MDC models today, there is also increasing attention on the analysis of bundle of mixed outcomes. The joint modeling of mixed outcomes is challenging because of the absence of a convenient multivariate distribution to jointly represent the relationship between discrete and continuous outcomes. The primary objective of this dissertation is to advance the econometric modeling of MDC choice situations, with an emphasis on two aspects of this modeling. The first is to include, in a general way, heterogeneity in the sensitivity to exogenous variables. The second is to extend the joint modeling of mixed outcomes to include MDC outcomes. These two modeling enhancements are undertaken through three specific objectives:(1) formulate and estimate a finite discrete mixture of normals (FDMN) version of the MDCP model (hybrid semi-parametric approach that combines a continuous response surface for the response coefficients with a latent class approach, allowing market segmentation in the MDC context), (2) formulate and estimate a spatial MDC model that considers a multivariate skew-normal (MVSN) distribution for the random coefficients (the MVSN distribution is tractable, parsimonious, and includes the normal distribution as a special interior point case), and (3) propose a new econometric approach for the estimation of joint mixed models that include an MDC outcome. The proposed enhancements are applied to different empirical contexts to analyze several choice processes within the transportation field.