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Author: Priyadarshan Nandkumar Patil
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
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Book Description
A simulation evaluation is presented to compare alternative estimation techniques for a five-alternative multinomial probit (MNP) model with random parameters, including cross-sectional and panel datasets and for scenarios with and without correlation among random parameters. The different estimation techniques assessed are: (1) The maximum approximate composite marginal likelihood (MACML) approach; (2) The Geweke-Hajivassiliou-Keane (GHK) simulator with Halton sequences, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; (3) The GHK approach with sparse grid nodes and weights, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; and (4) a Bayesian Markov Chain Monte Carlo (MCMC) approach. In addition, for comparison purposes, the GHK simulator with Halton sequences was implemented in conjunction with the traditional, full information maximum likelihood approach as well. The results indicate that the MACML approach provided the best performance in terms of the accuracy and precision of parameter recovery and estimation time for all data generation settings considered in this study. For panel data settings, the GHK approach with Halton sequences, when combined with the CML approach, provided better performance than when implemented with the full information maximum likelihood approach, albeit not better than the MACML approach. The sparse grid approach did not perform well in recovering the parameters as the dimension of integration increased, particularly so with the panel datasets. The Bayesian MCMC approach performed well in datasets without correlations among random parameters, but exhibited limitations in datasets with correlated parameters.
Author: Priyadarshan Nandkumar Patil
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
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Book Description
A simulation evaluation is presented to compare alternative estimation techniques for a five-alternative multinomial probit (MNP) model with random parameters, including cross-sectional and panel datasets and for scenarios with and without correlation among random parameters. The different estimation techniques assessed are: (1) The maximum approximate composite marginal likelihood (MACML) approach; (2) The Geweke-Hajivassiliou-Keane (GHK) simulator with Halton sequences, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; (3) The GHK approach with sparse grid nodes and weights, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; and (4) a Bayesian Markov Chain Monte Carlo (MCMC) approach. In addition, for comparison purposes, the GHK simulator with Halton sequences was implemented in conjunction with the traditional, full information maximum likelihood approach as well. The results indicate that the MACML approach provided the best performance in terms of the accuracy and precision of parameter recovery and estimation time for all data generation settings considered in this study. For panel data settings, the GHK approach with Halton sequences, when combined with the CML approach, provided better performance than when implemented with the full information maximum likelihood approach, albeit not better than the MACML approach. The sparse grid approach did not perform well in recovering the parameters as the dimension of integration increased, particularly so with the panel datasets. The Bayesian MCMC approach performed well in datasets without correlations among random parameters, but exhibited limitations in datasets with correlated parameters.
Author: Roman Liesenfeld
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Abstract: "We present a new specification for the multinomial multiperiod Probit model with autocorrelated errors. In sharp contrast with commonly used specifications, ours is invariant with respect to the choice of a baseline alternative for utility differencing. It also nests these standard models as special cases, allowing for data based selection of the baseline alternatives for the latter. Likelihood evaluation is achieved under an Efficient Importance Sampling (EIS) version of the standard GHK algorithm. Several simulation experiments highlight identification, estimation and pretesting within the new class of multinomial multiperiod Probit models." [author's abstract]
Author: Wagner A. Kamakura
Publisher:
ISBN:
Category :
Languages : en
Pages : 13
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Book Description
This study proposes the estimation of Multinomial Probit models using Mendell-Elston's approximation to the cumulative multivariate normal for the computation of choice probabilities. The accuracy of this numerical approximation in computing probabilities is compared with other procedures used in existing calibration programs. Finally, the proposed estimation procedure is tested on simulated choice data.
Author: Konstadinos G. Goulias
Publisher: Elsevier
ISBN: 0128173416
Category : Transportation
Languages : en
Pages : 736
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Book Description
Mapping the Travel Behavior Genome covers the latest research on the biological, motivational, cognitive, situational, and dispositional factors that drive activity-travel behavior. Organized into three sections, Retrospective and Prospective Survey of Travel Behavior Research, New Research Methods and Findings, and Future Research, the chapters of this book provide evidence of progress made in the most recent years in four dimensions of the travel behavior genome. These dimensions are Substantive Problems, Theoretical and Conceptual Frameworks, Behavioral Measurement, and Behavioral Analysis. Including the movement of goods as well as the movement of people, the book shows how traveler values, norms, attitudes, perceptions, emotions, feelings, and constraints lead to observed behavior; how to design efficient infrastructure and services to meet tomorrow's needs for accessibility and mobility; how to assess equity and distributional justice; and how to assess and implement policies for improving sustainability and quality of life. Mapping the Travel Behavior Genome examines the paradigm shift toward more dynamic, user-centric, demand-responsive transport services, including the "sharing economy," mobility as a service, automation, and robotics. This volume provides research directions to answer behavioral questions emerging from these upheavals. - Offers a wide variety of approaches from leading travel behavior researchers from around the world - Provides a complete map of the methods, skills, and knowledge needed to work in travel behavior - Describes the state of the art in travel behavior research, providing key directions for future research
Author: Shichao Tang
Publisher:
ISBN:
Category :
Languages : en
Pages : 216
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Book Description
In this dissertation, a potential outcomes (PO) based framework is developed for causally interpretable treatment effect parameters in the multinomial dependent variable regression framework. The specification of the relevant data generating process (DGP) is also derived. This new framework simultaneously accounts for the potential endogeneity of the treatment and loosens inter-category covariance restrictions on the multinomial outcome model (e.g., the independence from irrelevant alternatives restriction). Corresponding consistent estimators for the "deep parameters" of the DGP and the treatment effect parameters are developed and implemented (in Stata). A novel approach is proposed for assessing the inter-category covariance flexibility afforded by a particular multinomial modeling specification [e.g. multinomial logit (MNL), multinomial probit (MNP), and nested multinomial logit (NMNL)] in the context of our general framework. This assessment technique can serve as a useful tool for model selection. The new modeling/estimation approach developed in this dissertation is quite general. I focus here, however, on the NMNL model because, among the three modeling specifications under consideration (MNL, MNP and NMNL), it is the only one that is both computationally feasible and is relatively unrestrictive with regard to inter-category covariance. Moreover, as a logical starting point, I restrict my analyses to the simplest version of the model - the trinomial (three-category) NMNL with an endogenous treatment (ET) variable conditioned on individual-specific covariates only. To identify potential computational issues and to assess the statistical accuracy of my proposed NMNL-ET estimator and its implementation (in Stata), I conducted a thorough simulation analysis. I found that conventional optimization techniques are, in this context, generally fraught with convergence problems. To overcome this, I implement a systematic line search algorithm that successfully resolves this issue. The simulation results suggest that it is important to accommodate both endogeneity and inter-category covariance simultaneously in model design and estimation. As an illustration and as a basis for comparing alternative parametric specifications with respect to ease of implementation, computational efficiency and statistical performance, the proposed model and estimation method are used to analyze the impact of substance abuse/dependence on the employment status using the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) data.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
The multinomial probit (MNP) model offers a rather general and flexible framework for the analysis of discrete choices obtained from panel data and the specification of models with general error structures. However, this flexibility of specification has come at a relatively high price in terms of difficulty of computing maximum likelihood estimates ofthe model parameters and evaluating the associated choice function. This paper presents a new procedure for the estimation of MNP models, motivated primarily by the advances that have taken place in terms of computing environments and capabilities. the procedure derives its efficiency from execution in a parallel computing environment (CRAY YMP/8) and its accuracy from the use of better (but otherwise more computationally intensive) mathematical procedures. The paper also discusses and compares alternative nonlinear optimization procedures to search for the parameter values that maximize the likelihood function, in connection with Monte Carlo evaluation of the likelihood of each observation. The results are also compared to those obtained using the Clark approximation to evaluate the choice probabilities.
Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399
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Book Description
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.
Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 9780521017152
Category : Business & Economics
Languages : en
Pages : 346
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Book Description
Table of contents
Author: Roberto Mariano
Publisher: Cambridge University Press
ISBN: 9780521591126
Category : Business & Economics
Languages : en
Pages : 488
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Book Description
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
Author: Kenneth E. Train
Publisher: Cambridge University Press
ISBN: 1139480375
Category : Business & Economics
Languages : en
Pages : 399
Get Book Here
Book Description
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. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This 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.
