Download or read book Bayesian Analysis of the Multivariate Probit Model written by Siddhartha Chib and published by . This book was released on 1995 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analysis of Multivariate Probit Models written by Siddhartha Chib and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Monte Carlo version of the EM algorithm. Computation of Bayes factors from the simulation output is also considered. The methods are applied to a bivariate data set, to a 534-subject, four-year longitudinal data set from the Six Cities study of the health effects of air pollution, and to a seven-year data set on the labor supply of married women from the Panel Survey of Income Dynamics.

Download or read book A Bayesian Approach to Spatial Correlations in the Multivariate Probit Model written by Jervyn Ang and published by . This book was released on 2010 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ordered categorical data arise in many applied settings. For example, many surveys have responses that may be restricted to 2Strongly Disagree3, 2Disagree, 2Neutral3, 2Agree3, and 2Strongly Agree3. Here, the responses are ordinal variables. That is, the agreeability of respondents to questions have relative ranks, but there is no measure of exact magnitude like there is with continuous variables. In many scenarios, questions may have correlated responses. As well, different respondents may be spatially or otherwise correlated. Probit models are a means to using normal latent variables in modelling ordinal responses. In this project, we take a Bayesian approach and include both 2between question3 and 2between respondent3 correlations in a multivariate probit model. We discuss the efficacy of this spatial multivariate probit model.

Download or read book Introduction to Applied Bayesian Statistics and Estimation for Social Scientists written by Scott M. Lynch and published by Springer Science & Business Media. This book was released on 2007-06-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.

Download or read book Bayesian Analysis of Non linear Multivariate Econometric Models written by Rong Zhang and published by . This book was released on 2011 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis aims to investigate Bayesian sampling techniques for estimating parameters of three nonlinear models with different levels of endogeneity and sample selection. These models include a bivariate probit model with an endogenous dummy regressor, an ordered probit model with sample selection, and an ordered probit model with double hurdles of sample selection. We developed Bayesian sampling algorithms to sample parameters in each of these models, and the resulting posterior estimates of parameters were compared with those obtained through a few classical estimation methods such as maximum likelihood estimate (MLE) and a two-step method. Monte Carlo simulations were conducted to check the performance of different estimators for each model.In the bivariate probit model with an endogenous dummy regressor, we discussed the identification conditions especially the effect of exclusion restrictions. The Monte Carlo study reveals that exclusion restrictions are not essential for model identification. However, the existence of exclusion restrictions will make the estimation much easier for all estimators. Moreover, model identification can be improved by increasing the variation of explanatory variables and the number of exogenous regressors. In terms of the performance of the three estimators, MLE is often accurate and efficient except for occasional convergence failures. The Bayesian method can always produce an estimate for each simulated sample and is most efficient. However, it shows same small bias when the correlation coefficient between errors is large. The inconsistent two-step method has less convergence problems than MLE, but has quite large biases when the correlation coefficient between errors is large.In terms of the ordered probit model with binary selection, we used a reparameterization to derive a Gibbs sampler, such that conditional posteriors can be obtained. We also propose a likelihood-based two-step method in a way similar to the derivation of the concentrated likelihood function. The two proposed methods were compared with the full information maximum likelihood (FIML) method and another established two-step method. Monte Carlo results show that the Bayesian method and the likelihood-based two-step method can be alternative methods to FIML, while the other two-step method is not acceptable in models with large error correlation. The absence of exclusion restrictions does not cause big problems for the model estimation. With the FIML and the Bayesian methods, we used the ordered probit model with binary selectivity to model the effect of mental illness on employment and job categories, where exclusion restrictions do not exist.The ordered probit model with double-hurdle selection is an extension of the above model with one additional level of sample selection. We found that FIML has encountered severe convergence-failure problems as the model becomes more complicated. As such, the proposed Bayesian sampling method is of great value because it always produces an estimate of the parameter vector. We propose two Bayesian samplers, one obtained through a standard process currently available in the literature, while the other involved reparameterization. In the Monte Carlo study, we found that both samplers and the FIML provide unbiased and efficient estimates. However, FIML fails to converge for more than half of the simulated samples, while Bayesian samplers can always produce estimates for each simulated sample. The reparameterization-based sampler shows better convergence than the other sampler. We applied the three estimators to the estimation of the double-hurdle ordered probit model investigating the effect of mental illness on labor market outcomes. We found that reparameterization-based sampler is the only estimator that did not encounter convergence problems. The resulting estimates of parameters were used for analyzing marginal effects of mental health variables.

Download or read book Data Augmentation in the Bayesian Multivariate Probit Model written by Roberto León-González 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 Bayesian Analysis of Hierarchical Models for Polychotomous Data from a Multi stage Cluster Sample written by Michael Edward Schuckers and published by . This book was released on 1999 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we present a hierarchical Bayesian methodology for analyzing polychotomous data from multi-stage cluster samples. We begin with a model for multinomial data drawn from a two-stage cluster sample of a finite population. This model is then extended to incorporate partially observed data assuming that the data are missing at random (MAR), in the terminology of Little and Rubin (1987). We next develop a model for polychotomous data collected via a three-stage cluster sample. As with the two-stage model, we describe the methodology for dealing with partially observed data assuming they are MAR. We apply these two methodologies to the 1990 Slovenian Public Opinion Survey and present the results of these analyses. Finally, we fashion a multivariate probit model for a special type of multinomial data, multivariate binary data. We then construct this model that incorporates covariate information for the case of a two-stage cluster sample. Specifically, we outline this methodology for a two-stage cluster sample. This approach also allows for the integration of missing data into the analysis if the data are MAR. For all of the above models we use Markov chain Monte Carlo techniques to simulate samples from the posterior distribution. These samples are then utilized in making inference from the models.

Download or read book A Bayesian Analysis of the Multinomial Probit Model written by Robert Edward McCulloch and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Models for Categorical Data written by Peter Congdon and published by John Wiley & Sons. This book was released on 2005-12-13 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.

Download or read book Applied Multivariate Analysis written by S. James Press and published by Courier Corporation. This book was released on 2012-09-05 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications. Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations, with particular focus on multidimensional scaling and clustering. In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.

Download or read book Modeling Ordered Choices written by William H. Greene and published by Cambridge University Press. This book was released on 2010-04-08 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.

Download or read book Policy Tool Bundling written by Anthony J. Kassekert and published by . This book was released on 2010 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: The choice of economic development incentives involves a complex system of political and economic considerations. Policy tools theory has largely focused on the individual characteristics of each particular tool and has not considered interactions among instruments or explained why multiple tools are used simultaneously in practice. Extant research has overlooked interdependence among policies and the fact that policies may serve as substitutes or compliments to each other. Building on theories of policy tools and policy diffusion, a theory of policy bundling is developed in this dissertation to explain why multiple tools are used in conjunction with one another to solve public problems. A diverse set of motivations and strategies are formed to explicate why bundling occur. The theory of policy tool bundling is empirically tested using panel data from the state of Georgia. The presence of policy tool bundling is assessed by modeling four economic incentives simultaneously with a multivariate probit model estimated using Bayesian methods. The results demonstrate that bundling is occurring between free or reduced cost land and expedited permitting and also between free or reduced cost land and industrial development bonds. No evidence of bundling was found between other incentives indicating that while policy bundling does occur in economic development, many of the observed relationships between policies are not strategic.

Download or read book Simulation based Inference in Econometrics written by Roberto Mariano and published by Cambridge University Press. This book was released on 2000-07-20 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bayesian Analysis in Statistics and Econometrics written by Donald A. Berry and published by John Wiley & Sons. This book was released on 1996 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a definitive work that captures the current state of knowledge of Bayesian Analysis in Statistics and Econometrics and attempts to move it forward. It covers such topics as foundations, forecasting inferential matters, regression, computation and applications.

Download or read book A Hybrid Markov Chain for the Bayesian Analysis of the Multinomial Probit Model written by Agostino Nobile and published by . This book was released on 1995 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics written by Gary Koop and published by Now Publishers Inc. This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.

Download or read book Missing Data in Longitudinal Studies written by Michael J. Daniels and published by CRC Press. This book was released on 2008-03-11 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ