Download or read book A Bayesian Procedure for Forecasting with Vector Autoregressions and Forecasting with Bayesian Vector Autoregressions four Years of Experience written by Robert B. Litterman and published by . This book was released on 1985 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Bayesian Procedure for Forecasting with Vector Autoregressions written by Robert B. Litterman and published by . This book was released on 1980 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Forecasting with Bayesian Vector Autoregressions written by Robert B. Litterman and published by . This book was released on 1985 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 Bayesian Vars written by Mr.Matteo Ciccarelli and published by International Monetary Fund. This book was released on 2003-05-01 with total page 47 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 Forecasting and Conditional Projection Using Realistic Prior Distributions written by Thomas Doan and published by . This book was released on 1986 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book BAYESIAN VECTOR AUTOREGRESSIVE MODELS FOR BUSINESS FORECASTING VAR BVAR written by Elizabeth Louise Rose and published by . This book was released on 1989 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: forecast comparisons.
Download or read book Forecasting Performance of Bayesian Vector Autoregression Models written by Raluca Alina Cata and published by . This book was released on 2007 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Macroeconomic Forecasting in the Era of Big Data written by Peter Fuleky and published by Springer Nature. This book was released on 2019-11-28 with total page 716 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.
Download or read book Bayesian Vector Autoregressive Models for Business Forecasting written by Elizabeth Louise Rose and published by . This book was released on 1991 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting with Bayesian Vector Autoregressions written by Faruk Selc̦uk and published by . This book was released on 1992 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Large Vector Autoregressions with Stochastic Volatility and Flexible Priors written by Andrea Carriero and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Large Bayesian Vector Autoregressions written by Joshua Chan 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 Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
