Bayesian Analysis of switching regression models
Author | : Michel Lubrano |
Publisher | : |
Total Pages | : 27 |
Release | : 1983 |
Genre | : |
ISBN | : |
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Author | : Michel Lubrano |
Publisher | : |
Total Pages | : 27 |
Release | : 1983 |
Genre | : |
ISBN | : |
Author | : Maria Ana E. Odejar |
Publisher | : |
Total Pages | : 228 |
Release | : 1998 |
Genre | : |
ISBN | : |
Author | : Broemeling |
Publisher | : CRC Press |
Total Pages | : 472 |
Release | : 2017-11-22 |
Genre | : Mathematics |
ISBN | : 1351464485 |
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 | : You Beng Koh |
Publisher | : |
Total Pages | : |
Release | : 2017-01-26 |
Genre | : |
ISBN | : 9781361301173 |
This dissertation, "Bayesian Analysis in Markov Regime-switching Models" by You Beng, Koh, 辜有明, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: van Norden and Schaller (1996) develop a standard regime-switching model to study stock market crashes. In their seminal paper, they use the maximum likelihood estimation to estimate the model parameters and show that a two-regime speculative bubble model has significant explanatory power for stock market returns in some observed periods. However, it is well known that the maximum likelihood estimation can lead to bias if the model contains multiple local maximum points or the estimation starts with poor initial values. Therefore, a better approach to estimate the parameters in the regime-switching models is to be found. One possible way is the Bayesian Gibbs-sampling approach, where its advantages are well discussed in Albert and Chib (1993). In this thesis, the Bayesian Gibbs-sampling estimation is examined by using two U.S. stock datasets: CRSP monthly value-weighted index from Jan 1926 to Dec 2010 and S&P 500 index from Jan 1871 to Dec 2010. It is found that the Gibbs-sampling estimation explains the U.S. data better than the maximum likelihood estimation. Moreover, the existing standard regime-switching speculative behaviour model is extended by considering the time-varying transition probabilities which are governed by the first-order Markov chain. It is shown that the time-varying first-order transition probabilities of Markov regime-switching speculative rational bubbles can lead stock market returns to have a second-order Markov regime. In addition, a Bayesian Gibbs-sampling algorithm is developed to estimate the parameters in the second-order two-state Markov regime-switching model. DOI: 10.5353/th_b4852164 Subjects: Bayesian statistical decision theory Markov processes
Author | : Donald A. Berry |
Publisher | : Wiley-Interscience |
Total Pages | : 618 |
Release | : 1996 |
Genre | : Business & Economics |
ISBN | : |
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.
Author | : Xiaofeng Wang |
Publisher | : CRC Press |
Total Pages | : 312 |
Release | : 2018-01-29 |
Genre | : Mathematics |
ISBN | : 1351165755 |
INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work. Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic. Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York. Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.
Author | : Kazuhiro Ohtani |
Publisher | : |
Total Pages | : 23 |
Release | : 1981 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Author | : Sylvia Frühwirth-Schnatter |
Publisher | : Springer Science & Business Media |
Total Pages | : 506 |
Release | : 2006-11-24 |
Genre | : Mathematics |
ISBN | : 0387357688 |
The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.
Author | : Roland Shami |
Publisher | : LAP Lambert Academic Publishing |
Total Pages | : 208 |
Release | : 2010-05 |
Genre | : |
ISBN | : 9783838363721 |
A new class of models based on the innovations form of structural models underlying exponential smoothing methods and a latent Markov switching process is proposed. Firstly, the local level model with a switching drift is introduced where the drift is represented by a variable that evolves according to a Markov chain and describes the change between high and low growth rate periods. One drift coefficient represents the expected rate of growth during an expansion and the other drift coefficient represents the expected rate during a recession. The transition probabilities of the Markov chain are constant. Then, the model is extended to a drift that is dependent on a leading economic indicator which leads to varying transition probabilities. A new Bayesian procedure, using a mixture of forward and backward filtering iterations, is developed to produce exact Bayesian posterior parameter and forecast distributions. The two models are applied to quarterly real US GNP data, considered as the main (coincident) indicator of economic health, to infer and forecast the US business cycle.
Author | : Lyle D. Broemeling |
Publisher | : |
Total Pages | : 480 |
Release | : 1985 |
Genre | : Mathematics |
ISBN | : |