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Author : Andriy Norets
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (93 download)
Download or read book Bayesian Inference in Dynamic Discrete Choice Models written by Andriy Norets and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Luc Bauwens
Publisher : Oxford University Press
ISBN 13 : 0198773137
Total Pages : 370 pages
Book Rating : 4.1/5 (987 download)
Download or read book Bayesian Inference in Dynamic Econometric Models written by Luc Bauwens and published by Oxford University Press. This book was released on 1999 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques basedon simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditionalheteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.
Author : Susumu Imai
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Bayesian Estimation of Dynamic Discrete Choice Models written by Susumu Imai and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though per solution-estimation iteration, the number of grid points on the state variable is small, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "Curse of Dimensionality." We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.
Author : Andriy Norets
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
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 Bayesian Estimation of Dynamic Discrete Choice Models written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Roberto Mariano
Publisher : Cambridge University Press
ISBN 13 : 9780521591126
Total Pages : 488 pages
Book Rating : 4.5/5 (911 download)
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.
Author : Kenneth Train
Publisher : Cambridge University Press
ISBN 13 : 0521766559
Total Pages : 399 pages
Book Rating : 4.5/5 (217 download)
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.
Author : Peter Haan
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (794 download)
Download or read book Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models written by Peter Haan and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Daniel Edward Houser
Publisher :
ISBN 13 :
Total Pages : 402 pages
Book Rating : 4.:/5 (319 download)
Download or read book Bayesian Analysis of a Dynamic, Stochastic Model of Labor Supply and Saving written by Daniel Edward Houser and published by . This book was released on 1998 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Andrew T. Ching
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models written by Andrew T. Ching and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides a step-by-step guide to estimating infinite horizon discrete choice dynamic programming (DDP) models using a new Bayesian estimation algorithm (Imai, Jain and Ching, Econometrica 77:1865-1899, 2009) (IJC). In the conventional nested fixed point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the IJC algorithm extensively uses the computational results obtained from the past iterations to help solve the DDP model at the current iterated parameter values. Consequently, it has the potential to significantly alleviate the computational burden of estimating DDP models. To illustrate this new estimation method, we use a simple dynamic store choice model where stores offer "frequent-buyer" type reward programs. We show that the parameters of this model, including the discount factor, are well-identified. Our Monte Carlo results demonstrate that the IJC method is able to recover the true parameter values of this model quite precisely. We also show that the IJC method could reduce the estimation time significantly when estimating DDP models with unobserved heterogeneity, especially when the discount factor is close to 1.
Author : Ming-Hui Chen
Publisher : Springer Science & Business Media
ISBN 13 : 1441969446
Total Pages : 631 pages
Book Rating : 4.4/5 (419 download)
Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Author : Stephane Hess
Publisher : Edward Elgar Publishing
ISBN 13 : 1800375638
Total Pages : 797 pages
Book Rating : 4.8/5 (3 download)
Download or read book Handbook of Choice Modelling written by Stephane Hess and published by Edward Elgar Publishing. This book was released on 2024-06-05 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly revised second edition Handbook provides an authoritative and in-depth overview of choice modelling, covering essential topics range from data collection through model specification and estimation to analysis and use of results. It aptly emphasises the broad relevance of choice modelling when applied to a multitude of fields, including but not limited to transport, marketing, health and environmental economics.
Author : Kenneth E. Train
Publisher : Cambridge University Press
ISBN 13 : 1139480375
Total Pages : 399 pages
Book Rating : 4.1/5 (394 download)
Download or read book Discrete Choice Methods with Simulation written by Kenneth E. Train and published by Cambridge University Press. This book was released on 2009-06-30 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. 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.
Author : John Geweke
Publisher : OUP UK
ISBN 13 : 0199559082
Total Pages : 572 pages
Book Rating : 4.1/5 (995 download)
Download or read book The Oxford Handbook of Bayesian Econometrics written by John Geweke and published by OUP UK. This book was released on 2011-09-29 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.
Author : Steffen Christ
Publisher : Springer Science & Business Media
ISBN 13 : 3834961841
Total Pages : 363 pages
Book Rating : 4.8/5 (349 download)
Download or read book Operationalizing Dynamic Pricing Models written by Steffen Christ and published by Springer Science & Business Media. This book was released on 2011-04-02 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.
Download or read book Bayesian Inference in Dynamic Econometric Models written by and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series
Author : Sandeep R. Chandukala
Publisher : Now Publishers Inc
ISBN 13 : 1601981643
Total Pages : 100 pages
Book Rating : 4.6/5 (19 download)
Download or read book Choice Models in Marketing written by Sandeep R. Chandukala and published by Now Publishers Inc. This book was released on 2008 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Choice Models in Marketing examines recent developments in the modeling of choice for marketing and reviews a large stream of research currently being developed by both quantitative and qualitative researches in marketing. Choice in marketing differs from other domains in that the choice context is typically very complex, and researchers' desire knowledge of the variables that ultimately lead to demand in marketplace. The marketing choice context is characterized by many choice alternatives. The aim of Choice Models in Marketing is to lay out the foundations of choice models and discuss recent advances. The authors focus on aspects of choice that can be quantitatively modeled and consider models related to a process of constrained utility maximization. By reviewing the basics of choice modeling and pointing to new developments, Choice Models in Marketing provides a platform for future research.
