Bayesian Econometric Methods
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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.
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.
Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility models, ARCH, GARCH, and vector autoregressive models. The authors have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based and hierarchical specifications, models based upon latent variable representations, and mixture and time series specifications. MCMC methods are discussed and illustrated in detail - from introductory applications to those at the current research frontier - and MATLAB® computer programs are provided on the website accompanying the text. Suitable for graduate study in economics, the text should also be of interest to students studying statistics, finance, marketing, and agricultural economics.
The Oxford Handbook Of Bayesian Econometrics
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Author : John Geweke
language : en
Publisher: Oxford University Press, USA
Release Date : 2011-09-29
The Oxford Handbook Of Bayesian Econometrics written by John Geweke and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-29 with Business & Economics categories.
A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.
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.
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.
Essays On Semi Parametric Bayesian Econometric Methods
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Author : Ruochen Wu
language : en
Publisher:
Release Date : 2019
Essays On Semi Parametric Bayesian Econometric Methods written by Ruochen Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Econometric Methods And Applications
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Author : G. S. Maddala
language : en
Publisher: Edward Elgar Publishing
Release Date : 1994
Econometric Methods And Applications written by G. S. Maddala and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Business & Economics categories.
Brings together G.S. Maddala's contributions to economics. Among the articles reprinted here are those on the bituminous coal industry, his criticism of the pseudo-data approach and size distribution of incomes. Each part offers a reconsideration of the issues in terms of recent developments.
Econometric Methods
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Author : John Johnston
language : en
Publisher: McGraw-Hill Companies
Release Date : 1984
Econometric Methods written by John Johnston and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Business & Economics categories.
In writing this new edition we have had two major objectives. The first is to provide a comprehensive and accessible account of available econometric methods. The second is to illustrate these methods with applications to some real data sets, which are given on the data diskette that accompanies the book; thus, the reader can replicate the applications in the text, experiment with some of the problems suggested at the chapter ends, and carry out further analyses of her own choosing.
Using Simulation Methods For Bayesian Econometric Models
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Author : John Geweke
language : en
Publisher:
Release Date : 1998
Using Simulation Methods For Bayesian Econometric Models written by John Geweke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Bayesian statistical decision theory categories.
Bayesian Econometric Modelling For Big Data
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Author : Hang Qian
language : en
Publisher: CRC Press
Release Date : 2025-06-20
Bayesian Econometric Modelling For Big Data written by Hang Qian and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-20 with Mathematics categories.
This book delves into scalable Bayesian statistical methods designed to tackle the challenges posed by big data. It explores a variety of divide-and-conquer and subsampling techniques, seamlessly integrating these scalable methods into a broad spectrum of econometric models. In addition to its focus on big data, the book introduces novel concepts within traditional statistics, such as the summation, subtraction, and multiplication of conjugate distributions. These arithmetic operators conceptualize pseudo data in the conjugate prior, sufficient statistics that determine the likelihood, and the posterior as a balance between data and prior information, adding an intriguing dimension to Bayesian analysis. This book also offers a deep dive into Bayesian computation. Given the intricacies of floating-point representation of real numbers, computer programs can sometimes yield unexpected or theoretically impossible results. Drawing from his experience as a senior statistical software developer, the author shares valuable strategies for designing numerically stable algorithms. The book is an essential resource for a diverse audience: graduate students seeking foundational knowledge in Bayesian econometric models, early-career statisticians eager to explore cutting-edge advancements in scalable Bayesian methods, data analysts struggling with out-of-memory challenges in large datasets, and statistical software users and developers striving to program with efficiency and numerical stability.