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Author :
Publisher :
ISBN 13 :
Total Pages : 33 pages
Book Rating : 4.:/5 (931 download)
Download or read book Bayesian Analysis of Instrumental Variable Models written by and published by . This book was released on 2012 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 33 pages
Book Rating : 4.:/5 (931 download)
Download or read book Bayesian Analysis of Instrumental Variable Models written by and published by . This book was released on 2012 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Aart Kraay
Publisher : World Bank Publications
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4./5 ( download)
Download or read book Instrumental Variables Regressions with Honestly Uncertain Exclusion Restrictions written by Aart Kraay and published by World Bank Publications. This book was released on 2008 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The validity of instrumental variables (IV) regression models depends crucially on fundamentally untestable exclusion restrictions. Typically exclusion restrictions are assumed to hold exactly in the relevant population, yet in many empirical applications there are reasonable prior grounds to doubt their literal truth. In this paper I show how to incorporate prior uncertainty about the validity of the exclusion restriction into linear IV models, and explore the consequences for inference. In particular I provide a mapping from prior uncertainty about the exclusion restriction into increased uncertainty about parameters of interest. Moderate prior uncertainty about exclusion restrictions can lead to a substantial loss of precision in estimates of structural parameters. This loss of precision is relatively more important in situations where IV estimates appear to be more precise, for example in larger samples or with stronger instruments. The author illustrates these points using several prominent recent empirical papers that use linear IV models.
Author : Gary Chamberlain
Publisher :
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4.:/5 (321 download)
Download or read book Hierarchical Bayes Models with Many Instrumental Variables written by Gary Chamberlain and published by . This book was released on 1996 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we explore Bayesian inference in models with many instrumental variables that are potentially weakly correlated with the endogenous regressor. The prior distribution has a hierarchical (nested) structure. We apply the methods to the Angrist-Krueger (AK, 1991) analysis of returns to schooling using instrumental variables formed by interacting quarter of birth with state/year dummy variables. Bound, Jaeger, and Baker (1995) show that randomly generated instrumental variables, designed to match the AK data set, give two-stage least squares results that look similar to the results based on the actual instrumental variables. Using a hierarchical model with the AK data, we find a posterior distribution for the parameter of interest that is tight and plausible. Using data with randomly generated instruments, the posterior distribution is diffuse. Most of the information in the AK data can in fact be extracted with quarter of birth as the single instrumental variable. Using artificial data patterned on the AK data, we find that if all the information had been in the interactions between quarter of birth and state/year dummies, then the hierarchical model would still have led to precise inferences, whereas the single instrument model would have suggested that there was no information in the data. We conclude that hierarchical modeling is a conceptually straightforward way of efficiently combining many weak instrumental variables.
Author : Hajime Watanabe
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book A Bayesian Instrumental Variable Model for Multinomial Choice with Correlated Alternatives written by Hajime Watanabe and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Endogeneity and correlated alternatives are major concerns to be addressed in travel behavior analysis. However, these issues have been rarely dealt with simultaneously in advanced discrete choice models. This study proposed a multinomial probit model that incorporates the instrumental variable method, namely, a fully parametric instrumental variable model for a multinomial choice. The proposed model has the following three characteristics: (1) it allows binary and/or continuous endogenous variables; (2) it allows any number of instrumental variables in each alternative; and (3) it allows positive and/or negative correlations between any choice alternatives. For parameter estimation, we also proposed a Bayesian Markov chain Monte Carlo algorithm that can be accommodated in more extended model structures. The simulation study demonstrated that the proposed model addressed endogeneity while allowing correlations between the choice alternatives. Meanwhile, the simulation also implied that the users need to pay attention to the setting of the prior distribution when an endogenous variable of interest is binary, even if the sample size is moderate. The proposed model will be a useful tool in disciplines in which both endogeneity and correlations between choice alternatives are major concerns.
Author : Matthew Jude Salois
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book A Generalized Bayesian Instrumental Variable Approach Under Student T-Distributed Errors with Application written by Matthew Jude Salois and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis is given of an instrumental variable model that allows for t-distributed errors in both the structural equation and the instrument equation. Specifically, the approach for dealing with t-distributed errors is extended to the Bayesian instrumental variable estimator by modelling the variance for each error using a Gamma distributed hierarchical prior. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm for the heteroskedastic case. An example using data illustrates the approach and shows that ignoring the presence of error terms with heavy tails in the instrument equation when it exists may lead to biased estimates.
Author : Arnold Zellner
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (535 download)
Download or read book Bayesian Method of Moments written by Arnold Zellner and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Broemeling
Publisher : CRC Press
ISBN 13 : 1351464485
Total Pages : 472 pages
Book Rating : 4.3/5 (514 download)
Download or read book Bayesian Analysis of Linear Models written by Broemeling and published by CRC Press. This book was released on 2017-11-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus, this definitive volume containssomething traditional-a review of Bayesian techniques and methods of estimation, hypothesis,testing, and forecasting as applied to the standard populations ... somethinginnovative-a new approach to mixed models and models not generally studied by statisticianssuch as linear dynamic systems and changing parameter models ... and somethingpractical-clear graphs, eary-to-understand examples, end-of-chapter problems, numerousreferences, and a distribution appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text isideal for students in graduate-level courses such as linear models, econometrics, andBayesian inference.
Author : Andrew Gelman
Publisher : John Wiley & Sons
ISBN 13 : 9780470090435
Total Pages : 448 pages
Book Rating : 4.0/5 (94 download)
Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-09-03 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
Author : Xuyang Lu
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.:/5 (889 download)
Download or read book Bayesian Approaches for Instrumental Variable Analysis with Censored Time-to-event Outcome written by Xuyang Lu and published by . This book was released on 2014 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: The method of instrumental variable (IV) analysis has been widely used in economics, epidemiology, and other fields to estimate the causal effects of intermediate covariates on outcomes, in the presence of unobserved confounders and/or measurement errors in covariates. Consistent estimation of the effect has been developed when the outcome is continuous, while methods for binary outcome produce inconsistent estimation. In this dissertation, we examine two IV methods in the literature for binary outcome and show the bias in parameter estimate by a simulation study. The identifiability problem of IV analysis with binary outcome is discussed. Moreover, IV methods for time-to-event outcome with censored data remain underdeveloped. We propose two Bayesian approaches for IV analysis with censored time-to-event outcome by using a two-stage linear model: One is a parametric Bayesian model with normal and non-normal elliptically contoured error distributions, and the other is a semiparametric Bayesian model with Dirichlet process mixtures for the random errors, in order to relax the parametric assumptions and address heterogeneous clustering problems. Markov Chain Monte Carlo sampling methods are developed for both parametric and semiparametric Bayesian models to estimate the endogenous parameter. Performance of our methods is examined by simulation studies. Both methods largely reduce bias in estimation and greatly improve coverage probability of the endogenous parameter, compared to the regular method where the unobserved confounders and/or measurement errors are ignored. We illustrate our methods on the Women's Health Initiative Observational Study and the Atherosclerosis Risk in Communities Study.
Author : Peter S. H. Leeflang
Publisher : Springer
ISBN 13 : 3319534696
Total Pages : 725 pages
Book Rating : 4.3/5 (195 download)
Download or read book Advanced Methods for Modeling Markets written by Peter S. H. Leeflang and published by Springer. This book was released on 2017-08-29 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents advanced techniques to modeling markets, with a wide spectrum of topics, including advanced individual demand models, time series analysis, state space models, spatial models, structural models, mediation, models that specify competition and diffusion models. It is intended as a follow-on and companion to Modeling Markets (2015), in which the authors presented the basics of modeling markets along the classical steps of the model building process: specification, data collection, estimation, validation and implementation. This volume builds on the concepts presented in Modeling Markets with an emphasis on advanced methods that are used to specify, estimate and validate marketing models, including structural equation models, partial least squares, mixture models, and hidden Markov models, as well as generalized methods of moments, Bayesian analysis, non/semi-parametric estimation and endogeneity issues. Specific attention is given to big data. The market environment is changing rapidly and constantly. Models that provide information about the sensitivity of market behavior to marketing activities such as advertising, pricing, promotions and distribution are now routinely used by managers for the identification of changes in marketing programs that can improve brand performance. In today’s environment of information overload, the challenge is to make sense of the data that is being provided globally, in real time, from thousands of sources. Although marketing models are now widely accepted, the quality of the marketing decisions is critically dependent upon the quality of the models on which those decisions are based. This volume provides an authoritative and comprehensive review, with each chapter including: · an introduction to the method/methodology · a numerical example/application in marketing · references to other marketing applications · suggestions about software. Featuring contributions from top authors in the field, this volume will explore current and future aspects of modeling markets, providing relevant and timely research and techniques to scientists, researchers, students, academics and practitioners in marketing, management and economics.
Author : Cheng Hsiao
Publisher : Cambridge University Press
ISBN 13 : 113943134X
Total Pages : 352 pages
Book Rating : 4.1/5 (394 download)
Download or read book Analysis of Panels and Limited Dependent Variable Models written by Cheng Hsiao and published by Cambridge University Press. This book was released on 1999-07-29 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important collection brings together leading econometricians to discuss advances in the areas of the econometrics of panel data. The papers in this collection can be grouped into two categories. The first, which includes chapters by Amemiya, Baltagi, Arellano, Bover and Labeaga, primarily deal with different aspects of limited dependent variables and sample selectivity. The second group of papers, including those by Nerlove, Schmidt and Ahn, Kiviet, Davies and Lahiri, consider issues that arise in the estimation of dyanamic (possibly) heterogeneous panel data models. Overall, the contributors focus on the issues of simplifying complex real-world phenomena into easily generalisable inferences from individual outcomes. As the contributions of G. S. Maddala in the fields of limited dependent variables and panel data were particularly influential, it is a fitting tribute that this volume is dedicated to him.
Author : Dingjing Shi
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (115 download)
Download or read book Bayesian Two-Stage Robust Causal Modeling with Instrumental Variables Using Student's T Distributions written by Dingjing Shi and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In causal inference research, the issue of the treatment endogeneity is commonly addressed using the two-stage least squares (2SLS) modeling with instrumental variables (IVs), where the local average treatment effect (LATE) is the causal effect of interest. Because practical data are usually heavy tailed or contain outliers, using traditional 2SLS modeling based on normality assumptions may result in inefficient or even biased LATE estimate. This study proposes four types of Bayesian two-stage robust causal models with IVs to model normal and nonnormal data, and evaluates the performance of the four types of models with IVs. The Monte Carlo simulation results show that the Bayesian two-stage robust causal modeling produces reliable parameter estimates and model fits. Particularly, in different types of the two-stage robust models with IVs, the models that take outliers into consideration and use Student's t distributions in the second stage to model heavy-tailed data or data containing outliers provide more accurate and efficient LATE estimates and better model fits than other distribution models when data are contaminated. The preferred models are recommended to be adopted in general in the two-stage causal modeling with IVs.
Author : Paul Gustafson
Publisher : CRC Press
ISBN 13 : 1439869405
Total Pages : 196 pages
Book Rating : 4.4/5 (398 download)
Download or read book Bayesian Inference for Partially Identified Models written by Paul Gustafson and published by CRC Press. This book was released on 2015-04-01 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs. The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification. This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide.
Author : Lyle D. Broemeling
Publisher :
ISBN 13 :
Total Pages : 480 pages
Book Rating : 4.3/5 (91 download)
Download or read book Bayesian Analysis of Linear Models written by Lyle D. Broemeling and published by . This book was released on 1985 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Neil R. Ericsson
Publisher :
ISBN 13 : 9780198774044
Total Pages : 436 pages
Book Rating : 4.7/5 (74 download)
Download or read book Testing Exogeneity written by Neil R. Ericsson and published by . This book was released on 1994 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the nature of exogeneity, a central concept in standard econometrics texts, and shows how to test for it through numerous substantive empirical examples from around the world, including the UK, Argentina, Denmark, Finland, and Norway. Part I defines terms and provides the necessary background; Part II contains applications to models of expenditure, money demand, inflation, wages and prices, and exchange rates; and Part III extends various tests of constancy and forecast accuracy, which are central to testing super exogeneity. About the Series Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
Author : Julianne Shan Swenson
Publisher :
ISBN 13 : 9781321350258
Total Pages : 179 pages
Book Rating : 4.3/5 (52 download)
Download or read book Contributions to Bayesian Statistics written by Julianne Shan Swenson and published by . This book was released on 2014 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: The instrumental variables method is a valuable tool in the analysis of simultaneous equations models. Since the estimation of coefficients in the model can be challenging, adept modeling of the covariance matrix is also important. The current standard Bayesian method utilizes an Inverse Wishart prior - a natural conjugate prior. We suggest a flexible prior specification that utilizes both location and scale prior information.
