Download or read book Bayesian VARs with Large Panels written by Marta Bańbura and published by . This book was released on 2007 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting with High Dimensional Panel Vars written by Gary Koop and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time-varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation-free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.
Download or read book Large Bayesian VARs written by Joshua C. C. Chan and published by . This book was released on 2015 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a class of large Bayesian vector autoregressions (BVARs) that allows for non-Gaussian, heteroscedastic and serially dependent innovations. To make estimation computationally tractable, we exploit a certain Kronecker structure of the likelihood implied by this class of models. We propose a unified approach for estimating these models using Markov chain Monte Carlo (MCMC) methods. In an application that involves 20 macroeconomic variables, we find that these BVARs with more flexible covariance structures outperform the standard variant with independent, homoscedastic Gaussian innovations in both in-sample model-fit and out-of-sample forecast performance.
Download or read book Bayesian Econometrics written by Siddhartha Chib and published by Emerald Group Publishing. This book was released on 2008-12-18 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
Download or read book Large Bayesian VARs written by Marta Bańbura and published by . This book was released on 2008 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Forecasting with Medium and Large Bayesian VARS written by Gary Koop and published by . This book was released on 2011 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series and Panel Data Econometrics written by M. Hashem Pesaran and published by Oxford University Press, USA. This book was released on 2015 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book describes and illustrates many advances that have taken place in a number of areas in theoretical and applied econometrics over the past four decades.
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 Current Trends in Bayesian Methodology with Applications written by Satyanshu K. Upadhyay and published by CRC Press. This book was released on 2015-05-21 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics. Each chapter is self-contained and focuses on a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples. This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.
Download or read book Minnesota type Adaptive Hierarchical Priors for Large Bayesian VARs 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 VARs Specification Choices and Forecast Accuracy written by Andrea Carriero and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we discuss how the forecasting performance of Bayesian VARs is affected by a number of specification choices. In the baseline case, we use a Normal-Inverted Wishart (N-IW) prior that, when combined with a (pseudo-) iterated approach, makes the analytical computation of h-step ahead forecasts feasible and simple, in particular when using standard and fixed values for the tightness and the lag length. We then assess the role of the optimal choice of the tightness, of the lag length and of both; compare alternative approaches to h-step ahead forecasting (direct, iterated and pseudo-iterated); discuss the treatment of the error variance and of cross-variable shrinkage; and address a set of additional issues, including the size of the VAR, modeling in levels or growth rates, and the extent of forecast bias induced by shrinkage. We obtain a large set of empirical results, but we can summarize them by saying that we find very small losses (and sometimes even gains) from the adoption of specification choices that make BVAR modeling quick and easy. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications.
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 48 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 Efficient Selection of Hyperparameters in Large Bayesian VARs Using Automatic Differentiation 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 Forecasting government bond yields with large Bayesian VARs written by Andrea Carriero and published by . This book was released on 2010 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Prior Selection for Panel Vector Autoregressions written by Dimitris Korobilis and published by . This book was released on 2015 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility written by Joshua Chan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
