Nonparametric Discrete Choice Models With Unobserved Heterogeneity

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Nonparametric Discrete Choice Models With Unobserved Heterogeneity

Author : Richard Briesch
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Nonparametric Discrete Choice Models With Unobserved Heterogeneity by : Richard Briesch

Download or read book Nonparametric Discrete Choice Models With Unobserved Heterogeneity written by Richard Briesch and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this research, we provide a new method to estimate discrete choice models with unobserved heterogeneity that can be used with either cross-sectional or panel data. The method imposes nonparametric assumptions on the systematic subutility functions and on the distributions of the unobservable random vectors and the heterogeneity parameter. The estimators are computationally feasible and strongly consistent. We provide an empirical application of the estimator to a model of store format choice. The key insights from the empirical application are: 1) consumer response to cost and distance contains interactions and non-linear effects which implies that a model without these effects tends to bias the estimated elasticities and heterogeneity distribution and 2) the increase in likelihood for adding non-linearities is similar to the increase in likelihood for adding heterogeneity, and this increase persists as heterogeneity is included in the model.

Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices

Author : Hiroyuki Kasahara
Publisher :
ISBN 13 : 9780771428081
Total Pages : 45 pages
Book Rating : 4.4/5 (28 download)

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Book Synopsis Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices by : Hiroyuki Kasahara

Download or read book Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices written by Hiroyuki Kasahara and published by . This book was released on 2006 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

On the Role of Unobserved Preference Heterogeneity in Discrete Choice Models of Labour Supply

Author : Daniele Pacifico
Publisher :
ISBN 13 :
Total Pages : 34 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis On the Role of Unobserved Preference Heterogeneity in Discrete Choice Models of Labour Supply by : Daniele Pacifico

Download or read book On the Role of Unobserved Preference Heterogeneity in Discrete Choice Models of Labour Supply written by Daniele Pacifico and published by . This book was released on 2014 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this paper is to analyse the impact of unobserved preference heterogeneity in empirical applications of discrete choice models of labour supply. Typically, unobserved heterogeneity is estimated either with continuous or discrete mixture models. However, in order to avoid estimation difficulties, most of the empirical analysis assumes a relatively constrained mixture, standard examples being models where only few coefficients are allowed to vary with independent normal distributions or with discrete distributions with few mass points. We compare labour supply elasticities obtained with these typical specifications of unobserved heterogeneity with those from a more general model that we are able to estimate through an EM algorithm for the nonparametric estimation of mixed models. Results show that labour supply elasticities change significantly with respect to a basic model without unobserved heterogeneity only when the joint distribution of the varying tastes is left completely unspecified.

Three Essays on the Application of Discrete Choice Models with Discrete-continuous Heterogeneity Distributions

Author : Chen Wang
Publisher :
ISBN 13 :
Total Pages : 226 pages
Book Rating : 4.:/5 (1 download)

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Book Synopsis Three Essays on the Application of Discrete Choice Models with Discrete-continuous Heterogeneity Distributions by : Chen Wang

Download or read book Three Essays on the Application of Discrete Choice Models with Discrete-continuous Heterogeneity Distributions written by Chen Wang and published by . This book was released on 2016 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unobserved heterogeneity is comprehensively acknowledged as an important feature to be considered in discrete choice modeling. Over the last decade, there were abundant studies showing the great outperformance of capturing unobserved heterogeneity of Mixed-Mixed Logit(MM-MNL) models. However, most empirical researches still use mixed logit(MIXL) models or latent class(LC) models which introduced strong assumptions on distributions of marginal utility. In this dissertation, a Mixed-Mixed Logit model(MM-MNL) that assumes a non-parametric mixing distribution for marginal utility is discussed. Consequently, three empirical studies solving different transportation problems are introduced.

Nonparametric Identification in Nonseparable Duration Models with Unobserved Heterogeneity

Author : Petyo Bonev
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Nonparametric Identification in Nonseparable Duration Models with Unobserved Heterogeneity by : Petyo Bonev

Download or read book Nonparametric Identification in Nonseparable Duration Models with Unobserved Heterogeneity written by Petyo Bonev and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Discrete Choice Methods with Simulation

Author : Kenneth Train
Publisher : Cambridge University Press
ISBN 13 : 0521766559
Total Pages : 399 pages
Book Rating : 4.5/5 (217 download)

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Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train

Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

Nonparametric Estimation in Random Coefficients Binary Choice Models

Author : Yuichi Kitamura
Publisher :
ISBN 13 :
Total Pages : 53 pages
Book Rating : 4.:/5 (436 download)

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Book Synopsis Nonparametric Estimation in Random Coefficients Binary Choice Models by : Yuichi Kitamura

Download or read book Nonparametric Estimation in Random Coefficients Binary Choice Models written by Yuichi Kitamura and published by . This book was released on 2008 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers random coefficients binary choice models. The main goal is to estimate the density of the random coefficients nonparametrically. This is an ill-posed inverse problem characterized by an integral transform. A new density estimator for the random coefficients is developed, utilizing Fourier-Laplace series on spheres. This approach offers a clear insight on the identification problem. More importantly, it leads to a closed form estimator formula that yields a simple plug-in procedure requiring no numerical optimization. The new estimator, therefore, is easy to implement in empirical applications, while being flexible about the treatment of unobserved heterogeneity. Extensions including treatments of non-random coefficients and models with endogeneity are discussed.

The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics

Author : Jeffrey Racine
Publisher : Oxford University Press
ISBN 13 : 0199857946
Total Pages : 562 pages
Book Rating : 4.1/5 (998 download)

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Book Synopsis The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics by : Jeffrey Racine

Download or read book The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics written by Jeffrey Racine and published by Oxford University Press. This book was released on 2014-04 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.

The EM Algorithm and Extensions

Author : Geoffrey J. McLachlan
Publisher : John Wiley & Sons
ISBN 13 : 0470191600
Total Pages : 399 pages
Book Rating : 4.4/5 (71 download)

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Book Synopsis The EM Algorithm and Extensions by : Geoffrey J. McLachlan

Download or read book The EM Algorithm and Extensions written by Geoffrey J. McLachlan and published by John Wiley & Sons. This book was released on 2007-11-09 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

Discrete Choice Under Risk with Limited Consideration

Author : Levon Barseghyan
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Discrete Choice Under Risk with Limited Consideration by : Levon Barseghyan

Download or read book Discrete Choice Under Risk with Limited Consideration written by Levon Barseghyan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is concerned with learning decision makers' preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model's semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases.

Nonparametric Identification of Discrete Choice Models

Author : John K. Dagsvik
Publisher :
ISBN 13 :
Total Pages : 13 pages
Book Rating : 4.:/5 (314 download)

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Book Synopsis Nonparametric Identification of Discrete Choice Models by : John K. Dagsvik

Download or read book Nonparametric Identification of Discrete Choice Models written by John K. Dagsvik and published by . This book was released on 1998 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:

On Econometrics with Flexible Assumptions for Asset Pricing and Discrete Choice Models

Author : Karina Otero
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (959 download)

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Book Synopsis On Econometrics with Flexible Assumptions for Asset Pricing and Discrete Choice Models by : Karina Otero

Download or read book On Econometrics with Flexible Assumptions for Asset Pricing and Discrete Choice Models written by Karina Otero and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 1 proposes a new approach to estimate general stationary diffusion processes that describe the evolution of unobserved arrival rates of credit events on sovereign bonds, allowing for arbitrary parametric drift and diffusion specifications. The solutions and transition processes for stationary diffusions are generally unknown in closed form and therefore standard maximum likelihood methods do not apply. Moreover, the arrival rates of credit events on sovereign bonds are unobservable and a direct nonparametric estimation does not work. Chapter 1 overcomes these challenges combining a semi-nonparametric estimator in the framework of the Efficient Method of Moments, and a reduced-form model for pricing sovereign bonds and credit default swaps. The application for Brazil sovereign assets explores the performance of the model under different specifications of the intensity process.Chapter 2 proves a nonparametric identification result for a stochastic dynamic discrete-choice game of incomplete information. The joint distribution of the private information and the stage game payoffs of the players are both assumed unknown for the econometrician and the private information across alternatives is allowed to have different distributions and be dependent. This setup poses a circularity problem in the identification strategy that has not been solved for dynamic games. Chapter 2 proposes a solution through exclusion restrictions and implied properties of the unknown functions. Under the assumptions that the distribution of the private shocks for the outside option is known and the outside option's shocks are independent of other shocks, the results jointly identify the stage game payoffs and the joint distribution of the private information.Chapter 3 proposes a new nonparametric identification strategy for static multiple choice models with random heterogeneity in unobservables. The strategy relies on functional properties of the sub-utilities and the distribution of the unobservables, a known payoff function for the outside option and exclusion restrictions for all but one alternative. This new strategy does not transform the multiple choice model into a set of binary models, does not need special regressors, additive separability on observables or differentiability conditions. Some ideas for this new identification strategy are borrowed from a theorem published in 1993 that intended to identify all the sub-utility functions but one and also the distribution of the shocks in differences. However, the proof of this published theorem is incorrect and (to the best of my knowledge) this chapter is the first literature pointing this out and providing a new proof of a different version of the theorem after modifications of its assumptions.

Finite Mixture and Markov Switching Models

Author : Sylvia Frühwirth-Schnatter
Publisher : Springer Science & Business Media
ISBN 13 : 0387357688
Total Pages : 506 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis Finite Mixture and Markov Switching Models by : Sylvia Frühwirth-Schnatter

Download or read book Finite Mixture and Markov Switching Models written by Sylvia Frühwirth-Schnatter and published by Springer Science & Business Media. This book was released on 2006-11-24 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.

Simultaneous Choice Models

Author : Natalia Lazzati
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Simultaneous Choice Models by : Natalia Lazzati

Download or read book Simultaneous Choice Models written by Natalia Lazzati and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study simultaneous choice models from a revealed preference approach given limited data. By limited data we mean that we observe a single equilibrium from the equilibrium set for a collection of related models or games. The objective of our analysis is twofold: We first use monotone and exclusion restrictions to provide out-of-sample predictions of equilibrium points. The predictions we offer take the form of intervals in lattices whose sharp bounds are characterized via monotone comparative statics. We then propose conditions on the data so that the empirical evidence can be rationalized as the Nash equilibria of a supermodular game. The approach we propose is nonparametric, allows for unobserved heterogeneity, and does not rely on any equilibrium selection mechanism.

Essays in Econometrics: Nonparametrics and Robustness

Author : Benjamin William Deaner
Publisher :
ISBN 13 :
Total Pages : 212 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Essays in Econometrics: Nonparametrics and Robustness by : Benjamin William Deaner

Download or read book Essays in Econometrics: Nonparametrics and Robustness written by Benjamin William Deaner and published by . This book was released on 2021 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heterogeneity and my key identifying assumptions follow from restrictions on the serial dependence structure.

Methods for Statistical Analysis and Prediction of Discrete Choices

Author : Remi Daviet
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Methods for Statistical Analysis and Prediction of Discrete Choices by : Remi Daviet

Download or read book Methods for Statistical Analysis and Prediction of Discrete Choices written by Remi Daviet and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing availability of individual-level consumer data has facilitated the development of new methods for analyzing and predicting people's product choices. This thesis contributes to the existing body of literature with three chapters advancing the statistical analysis of discrete choice. In Chapter 1, I propose a new model seeking to elaborate on the role that choice set composition, known as context, plays in a discrete choice problem. Specifically, I generalize a state-of-the-art class of models stemming from recent research on neural normalization to the multi-attribute setting. I impose a structural model composition based on the brain synaptic plasticity literature, allowing for a particular form of correlation between product utilities. I highlight the properties of the model with a series of experiments and real-world empirical applications. In Chapter 2, I propose a new Monte Carlo method, called Hamiltonian Sequential Monte Carlo (HSMC), for the purpose of nonparametric estimation of unobserved consumer heterogeneity in discrete choice problems. HSMC combines the advantages of Sequential Monte Carlo (SMC) and Hamiltonian transition dynamics. SMC exploits gains from parallelization very efficiently as the core computational load involving the model likelihood is performed by many individual particles independently of one another. At the same time, by using first-derivative information, Hamiltonian transition dynamics has been shown to yield substantial gains in Markov chain mixing properties relative to the standard random walk proposal steps outside of the SMC context. I compare the performance of HSMC with SMC on a discrete choice model of consumer purchases with nonparametric consumer heterogeneity and show the favorable properties of HSMC. In Chapter 3, I propose a Bayesian method called Sequential Optimal Inference (SOI) providing an optimal sequence of questions in experiments. Experiments are frequently used to elicit preferences of potential consumers. Using SOI, the questions are adaptively designed to maximize the expected information and take into account the subjects' previous answers. The method also allows for real-time inference and provides updated posterior distributions while the experiment is performed. Existing methods were created for specific sub-cases. SOI is more general, nesting a large class of models and allowing for a number of inference objectives.

A Generic Form for Capturing Unobserved Heterogeneity in Discrete Choice Modelling

Author : Ali Yazdizadeh
Publisher :
ISBN 13 :
Total Pages : 75 pages
Book Rating : 4.:/5 (111 download)

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Book Synopsis A Generic Form for Capturing Unobserved Heterogeneity in Discrete Choice Modelling by : Ali Yazdizadeh

Download or read book A Generic Form for Capturing Unobserved Heterogeneity in Discrete Choice Modelling written by Ali Yazdizadeh and published by . This book was released on 2016 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discrete choice models and their strength to predict individual choices mostly depend on the quality of datasets that have been used for model generation. However, even the most comprehensive and detailed datasets are not able to observe all factors pertinent to someone’s choice. This issue in the choice modelling literature has been addressed as unobserved heterogeneity, which means that individuals across populations are not affected identically by alternative attributes. Furthermore, such variation in preferences across populations and their sources are not always recognized by researchers. There are different methods to capture unobserved heterogeneity proposed in the discrete choice literature among which the random parameters approach, also referred to as mixed logit models, the latent class approach and the agent effect approach are the most well know methods. The main contribution of this study is to extend the formulation of LC-MMNL model to capture the agent effect by including a random term in the utility function of the model. Three types of models, Mixed Multinomial Logit (MMNL), Latent Class Mixed Multinomial Logit (LC-MMNL) and Agent Effect Latent Class Mixed Multinomial Logit (AGLC-MMNL) have been generated and the results compared. Considering agent effect simultaneously with other sources of unobserved heterogeneity in a latent class context demonstrates improvement in terms of model fit as well as cross section validation. It enables us to generate a latent class model with a larger number of classes explaining more heterogeneity across the population of a neighborhood location choice study. The AGLC-MMNL model is able to detect four distinct classes of individuals in Montreal, exhibiting different behaviours while facing neighborhood location choices in the context of a Discrete Choice Experiment. The classes of the model are able to explain different behaviours of individuals based on their income level, whether they are transit or car oriented, and the importance of privacy to them.