Download or read book The Effect of Bayesian Priors on the Moving average Representation of Vector Autoregressions written by James E. Kennedy and published by . This book was released on 1989 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Compound Dilemmas written by Michael Dean McGinnis and published by University of Michigan Press. This book was released on 2001 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: An explanation of how domestic support for U.S. defense expenditures was generated during the Cold War
Download or read book Structural Vector Autoregressive Analysis written by Lutz Kilian and published by Cambridge University Press. This book was released on 2017-11-23 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
Download or read book Bayesian Inference of Threshold Autoregressive Moving Average Models Using Sample based Methods written by Mahmoud M. Smadi and published by . This book was released on 1997 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Benchmark Priors Revisited written by Stefan Zeugner and published by International Monetary Fund. This book was released on 2009-09-01 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: Default prior choices fixing Zellner's g are predominant in the Bayesian Model Averaging literature, but tend to concentrate posterior mass on a tiny set of models. The paper demonstrates this supermodel effect and proposes to address it by a hyper-g prior, whose data-dependent shrinkage adapts posterior model distributions to data quality. Analytically, existing work on the hyper-g-prior is complemented by posterior expressions essential to fully Bayesian analysis and to sound numerical implementation. A simulation experiment illustrates the implications for posterior inference. Furthermore, an application to determinants of economic growth identifies several covariates whose robustness differs considerably from previous results.
Download or read book Multiple Time Series Modeling Using the SAS VARMAX Procedure written by Anders Milhoj and published by SAS Institute. This book was released on 2016-01-11 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aimed at econometricians who have completed at least one course in time series modeling, this comprehensive book will teach you the time series analytical possibilities that SAS offers today. --
Download or read book Forecasting with Bayesian Vector Autoregressions written by K. R. Kadiyala and published by . This book was released on 1989 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Fed in Print written by and published by . This book was released on 1989 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Journal of International Money and Finance written by and published by . This book was released on 1990 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Earlier place of publication varies.
Download or read book Bayesian Vars written by Matteo Ciccarelli and published by International Monetary Fund. This book was released on 2003-05 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregressive models (BVARs). After describing the Bayesian principle of estimation, we first present the methodology originally developed by Litterman (1986) and Doan et al. (1984) and review alternative priors. We then discuss extensions of the basic model and address issues in forecasting and structural analysis. An application to the estimation of a system of time-varying reaction functions for four European central banks under the European Monetary System (EMS) illustrates how some of the results previously presented may be applied in practice.
Download or read book Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage written by Deborah Gefang and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Estimation of DSGE Models written by Edward P. Herbst and published by Princeton University Press. This book was released on 2015-12-29 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Download or read book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics written by Gary Koop and published by Now Publishers Inc. This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Applied Bayesian Hierarchical Methods written by Peter D. Congdon and published by CRC Press. This book was released on 2010-05-19 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models involves complex data structures and is often described as a revolutionary development. An intermediate-level treatment of Bayesian hierarchical models and their applications, Applied Bayesian Hierarchical Methods demonstrates the advantages of a Bayesian approach
Download or read book Bayesian Vector Autoregressive Analysis written by Michał Markun and published by . This book was released on 2011 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: The dissertation investigates various aspects of Bayesian inference in time series econometrics. It consists of one expository chapter and two research papers. The first chapter presents on an easy example of a production function for the USA the development of Bayesian models in the context of time series analysis. The model analysed is the Cobb-Douglas production function with covariance stationary AR(1) disturbances. The methods presented are used extensively in the next two chapters. The first research paper tackles the issue of identifiation in a SVAR model with an error term being a Markov mixture of normal distributions. Non-Gaussianity can be employed for the identification of shocks. So far only classical methods have been proposed for this class of models. Bayesian methods for inference are presented, in particular an efficient method for testing homogeneity of shock process. An empirical example presents the workings of the tools developed. The topic of the second paper is the forecasting with Bayesian VARs. Owing to the shrinkage, the original Minnesota prior was reported to provide significant improvements in forecasting accuracy. Its limitations however, gave rise to research trying to relax restrictive treatment of the residual covariance matrix, and to allow for the possibility of cointegration in the system. This paper first disentangles in a unified framework and a balanced environment of optimizing choice of hyperparameters the impact on the predictive power of BVARs of developments of priors along the above two dimensions; a well known historical dataset is analyzed for this purpose. As the second contribution, the paper presents a novel prior characterized by explicit modelling of cointegration that avoids certain unattractive restrictive properties of the previously used priors; the potential of the prior for elicitation from the well established Litterman beliefs is demonstrated as well as predictive accuracy improvements over the benchmarks.
Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Download or read book The Oxford Handbook of Bayesian Econometrics written by John Geweke and published by Oxford University Press. This book was released on 2011-09-29 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
