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Author : Christian Aßmann
Publisher :
ISBN 13 :
Total Pages : 52 pages
Book Rating : 4.:/5 (87 download)
Download or read book Bayesian Analysis of Dynamic Factor Models written by Christian Aßmann and published by . This book was released on 2014 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Siem Jan Koopman
Publisher : Emerald Group Publishing
ISBN 13 : 1785603523
Total Pages : 685 pages
Book Rating : 4.7/5 (856 download)
Download or read book Dynamic Factor Models written by Siem Jan Koopman and published by Emerald Group Publishing. This book was released on 2016-01-08 with total page 685 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Author : Markus Pape
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (931 download)
Download or read book Model Identification in Bayesian Analysis of Static and Dynamic Factor Models written by Markus Pape and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Hairong Song
Publisher :
ISBN 13 :
Total Pages : 268 pages
Book Rating : 4.:/5 (83 download)
Download or read book Bayesian Analysis of Random Coefficient Dynamic Factor Models written by Hairong Song and published by . This book was released on 2009 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Laura Jackson Young
Publisher :
ISBN 13 :
Total Pages : 46 pages
Book Rating : 4.:/5 (13 download)
Download or read book Specification and Estimation of Bayesian Dynamic Factor Models written by Laura Jackson Young and published by . This book was released on 2019 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single- factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.
Author : Luc Bauwens
Publisher : OUP Oxford
ISBN 13 : 0191588466
Total Pages : 370 pages
Book Rating : 4.1/5 (915 download)
Download or read book Bayesian Inference in Dynamic Econometric Models written by Luc Bauwens and published by OUP Oxford. This book was released on 2000-01-06 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.
Author : Michael P. Clements
Publisher : OUP USA
ISBN 13 : 0195398645
Total Pages : 732 pages
Book Rating : 4.1/5 (953 download)
Download or read book The Oxford Handbook of Economic Forecasting written by Michael P. Clements and published by OUP USA. This book was released on 2011-07-08 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Author : Mike West
Publisher : Springer Science & Business Media
ISBN 13 : 1475793650
Total Pages : 720 pages
Book Rating : 4.4/5 (757 download)
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.
Download or read book Specification and Estimation of Bayesian Dynamic Factor Models written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Mr.Maxym Kryshko
Publisher : International Monetary Fund
ISBN 13 : 1463904215
Total Pages : 62 pages
Book Rating : 4.4/5 (639 download)
Download or read book Bayesian Dynamic Factor Analysis of a Simple Monetary DSGE Model written by Mr.Maxym Kryshko and published by International Monetary Fund. This book was released on 2011-09-01 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: When estimating DSGE models, the number of observable economic variables is usually kept small, and it is conveniently assumed that DSGE model variables are perfectly measured by a single data series. Building upon Boivin and Giannoni (2006), we relax these two assumptions and estimate a fairly simple monetary DSGE model on a richer data set. Using post-1983 U.S.data on real output, inflation, nominal interest rates, measures of inverse money velocity, and a large panel of informational series, we compare the data-rich DSGE model with the regular - few observables, perfect measurement - DSGE model in terms of deep parameter estimates, propagation of monetary policy and technology shocks and sources of business cycle fluctuations. We document that the data-rich DSGE model generates a higher implied duration of Calvo price contracts and a lower slope of the New Keynesian Phillips curve. To reduce the computational costs of the likelihood-based estimation, we employed a novel speedup as in Jungbacker and Koopman (2008) and achieved the time savings of 60 percent.
Author : Giovanni Petris
Publisher : Springer Science & Business Media
ISBN 13 : 0387772383
Total Pages : 258 pages
Book Rating : 4.3/5 (877 download)
Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Author : Ivan Jeliazkov
Publisher : John Wiley & Sons
ISBN 13 : 1118771125
Total Pages : 266 pages
Book Rating : 4.1/5 (187 download)
Download or read book Bayesian Inference in the Social Sciences written by Ivan Jeliazkov and published by John Wiley & Sons. This book was released on 2014-11-04 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
Author : John Geweke
Publisher : Oxford University Press, USA
ISBN 13 : 0199559082
Total Pages : 571 pages
Book Rating : 4.1/5 (995 download)
Download or read book The Oxford Handbook of Bayesian Econometrics written by John Geweke and published by Oxford University Press, USA. This book was released on 2011-09-29 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.
Author : Sylvia Kaufmann
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (84 download)
Download or read book Bayesian Estimation of Sparse Dynamic Factor Models with Order-independent Identification written by Sylvia Kaufmann and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Omar Aguilar
Publisher :
ISBN 13 :
Total Pages : 50 pages
Book Rating : 4.:/5 (388 download)
Download or read book Bayesian Dynamic Factor Models and Variance Matrix Discounting for Portfolio Allocation written by Omar Aguilar and published by . This book was released on 1998* with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Piotr Bialowolski
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (118 download)
Download or read book Bayesian Averaging Vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data written by Piotr Bialowolski and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Broemeling
Publisher : CRC Press
ISBN 13 : 1351464485
Total Pages : 472 pages
Book Rating : 4.3/5 (514 download)
Download or read book Bayesian Analysis of Linear Models written by Broemeling and published by CRC Press. This book was released on 2017-11-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
