Bayesian Analysis Of Multivariate Regime Switching Covariance Model PDF Download
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Author | : Lu Zhang |
Publisher | : |
Total Pages | : 117 |
Release | : 2011 |
Genre | : |
ISBN | : |
Download Bayesian Analysis of Multivariate Regime Switching Covariance Model Book in PDF, ePub and Kindle
Author | : Luigi Spezia |
Publisher | : |
Total Pages | : 0 |
Release | : 2009 |
Genre | : |
ISBN | : |
Download Bayesian Analysis of Multivariate Gaussian Hidden Markov Models with an Unknown Number of Regimes Book in PDF, ePub and Kindle
Multivariate Gaussian hidden Markov models with an unknown number of regimes are introduced here in the Bayesian setting and new efficient reversible jump Markov chain Monte Carlo algorithms for estimating both the dimension and the unknown parameters of the model are presented. Hidden Markov models are an extension of mixture models that can be applied to time series so as to classify the observations in a small number of groups, to understand when change points occur in the dynamics of the series and to model data heterogeneity through the switching among subseries with different means and covariance matrices. These aims can be achieved by assuming that the observed phenomenon is driven by a latent, or hidden, Markov chain. The methodology is illustrated through two different examples of multivariate time series.
Author | : You Beng Koh |
Publisher | : Open Dissertation Press |
Total Pages | : |
Release | : 2017-01-26 |
Genre | : |
ISBN | : 9781361301050 |
Download Bayesian Analysis in Markov Regime-Switching Models Book in PDF, ePub and Kindle
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 | : You Beng Koh |
Publisher | : |
Total Pages | : 0 |
Release | : 2012 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download Bayesian Analysis in Markov Regime-switching Models Book in PDF, ePub and Kindle
Author | : You Beng Koh |
Publisher | : |
Total Pages | : 298 |
Release | : 2012 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download Bayesian Analysis in Markov Regime-switching Models Book in PDF, ePub and Kindle
Author | : Sylvia Frühwirth-Schnatter |
Publisher | : Springer Science & Business Media |
Total Pages | : 506 |
Release | : 2006-11-24 |
Genre | : Mathematics |
ISBN | : 0387357688 |
Download Finite Mixture and Markov Switching Models Book in PDF, ePub and Kindle
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 | : Gary Koop |
Publisher | : Now Publishers Inc |
Total Pages | : 104 |
Release | : 2010 |
Genre | : Business & Economics |
ISBN | : 160198362X |
Download Bayesian Multivariate Time Series Methods for Empirical Macroeconomics Book in PDF, ePub and Kindle
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.
Author | : Roland George Shami |
Publisher | : |
Total Pages | : 390 |
Release | : 2001 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download Bayesian Analysis of a Structural Model with Regime Switching Book in PDF, ePub and Kindle
Author | : Daniel B. Rowe |
Publisher | : CRC Press |
Total Pages | : 350 |
Release | : 2002-11-25 |
Genre | : Mathematics |
ISBN | : 1420035266 |
Download Multivariate Bayesian Statistics Book in PDF, ePub and Kindle
Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but
Author | : Siddhartha Chib |
Publisher | : |
Total Pages | : 28 |
Release | : 1998 |
Genre | : Correlation (Statistics) |
ISBN | : |
Download Bayesian Analysis of Multivariate Count Data Book in PDF, ePub and Kindle