Bayesian Econometrics
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Bayesian Econometrics
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Author : Gary Koop
language : en
Publisher: Wiley-Interscience
Release Date : 2003
Bayesian Econometrics written by Gary Koop and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Business & Economics categories.
Researchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. Topics covered in the book include the regression model (and variants applicable for use with panel data), time series models, models for qualitative or censored data, nonparametric methods and Bayesian model averaging. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.
Introduction To Bayesian Econometrics
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Author : Edward Greenberg
language : en
Publisher: Cambridge University Press
Release Date : 2013
Introduction To Bayesian Econometrics written by Edward Greenberg and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Business & Economics categories.
This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.
Bayesian Econometric Methods
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Author : Joshua Chan
language : en
Publisher: Cambridge University Press
Release Date : 2019-08-15
Bayesian Econometric Methods written by Joshua Chan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-15 with Business & Economics categories.
Illustrates Bayesian theory and application through a series of exercises in question and answer format.
Contemporary Bayesian Econometrics And Statistics
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Author : John Geweke
language : en
Publisher: John Wiley & Sons
Release Date : 2005-10-03
Contemporary Bayesian Econometrics And Statistics written by John Geweke and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-03 with Mathematics categories.
Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including: * Linear models and policy choices * Modeling with latent variables and missing data * Time series models and prediction * Comparison and evaluation of models The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets. This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.
The Oxford Handbook Of Bayesian Econometrics
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Author : John Geweke
language : en
Publisher: Oxford University Press
Release Date : 2011-09-29
The Oxford Handbook Of Bayesian Econometrics written by John Geweke and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-29 with Business & Economics categories.
Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.
Bayesian Econometrics
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Author : Siddhartha Chib
language : en
Publisher: Emerald Group Publishing
Release Date : 2008-12-18
Bayesian Econometrics written by Siddhartha Chib and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-18 with Business & Economics categories.
Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
Introduction To Modern Bayesian Econometrics
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Author : Tony Lancaster
language : en
Publisher: Wiley-Blackwell
Release Date : 2004-06-18
Introduction To Modern Bayesian Econometrics written by Tony Lancaster and has been published by Wiley-Blackwell this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-18 with Business & Economics categories.
In this new and expanding area, Tony Lancaster’s text is the first comprehensive introduction to the Bayesian way of doing applied economics. Uses clear explanations and practical illustrations and problems to present innovative, computer-intensive ways for applied economists to use the Bayesian method; Emphasizes computation and the study of probability distributions by computer sampling; Covers all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data; Details causal inference and inference about structural econometric models; Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software Supported by online supplements, including Data Sets and Solutions to Problems, at www.blackwellpublishing.com/lancaster
Bayesian Inference In Dynamic Econometric Models
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Author : Luc Bauwens
language : en
Publisher: OUP Oxford
Release Date : 2000-01-06
Bayesian Inference In Dynamic Econometric Models written by Luc Bauwens and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-06 with Business & Economics categories.
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 based on 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 conditional heteroskedastic 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.
Bayesian Econometrics
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Author : Mauro Bernardi
language : en
Publisher: MDPI
Release Date : 2020-12-28
Bayesian Econometrics written by Mauro Bernardi and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-28 with Business & Economics categories.
Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb–Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.
Nonlinear Financial Econometrics Forecasting Models Computational And Bayesian Models
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Author : G. Gregoriou
language : en
Publisher: Springer
Release Date : 2010-12-21
Nonlinear Financial Econometrics Forecasting Models Computational And Bayesian Models written by G. Gregoriou and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-21 with Business & Economics categories.
This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.