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 121 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 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 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 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 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 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 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 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 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 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 Estimating Dynamic Discrete Choice Models with Hyperbolic Discounting with an Application to Mammography Decisions written by Hanming Fang and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We extend the semiparametric estimation method for dynamic discrete choice models using Hotz and Miller's (Review of Economic Studies 60 (1993), 497-529) conditional choice probability approach to the setting where individuals may have hyperbolic discounting time preferences and may be naive about their time inconsistency. We illustrate the proposed identification and estimation method with an empirical application of adult women's decisions to undertake mammography to evaluate the importance of present bias and naivety in the underutilization of this preventive health care. Our results show evidence for both present bias and naivety.

Download or read book Identification of Semiparametric Discrete Choice Models written by T. Scott Thompson and published by . This book was released on 1989 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: