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Bayesian Estimation of DSGE Models / Frank Schorfheide, Edward P. Herbst.

By: Contributor(s): Material type: TextTextSeries: The Econometric and Tinbergen Institutes LecturesPublisher: Princeton, NJ : Princeton University Press, [2015]Copyright date: ©2016Description: 1 online resource (296 p.) : 34 line illus. 23 tablesContent type:
Media type:
Carrier type:
ISBN:
  • 9780691161082
  • 9781400873739
Subject(s): DDC classification:
  • 339.501519542 23
LOC classification:
  • HB145 .H467 2017
Other classification:
  • online - DeGruyter
Online resources: Available additional physical forms:
  • Issued also in print.
Contents:
Frontmatter -- Contents -- Figures -- Tables -- Series Editors' Introduction -- Preface -- Part I. Introduction to DSGE Modeling and Bayesian Inference -- 1. DSGE Modeling -- 2. Turning a DSGE Model into a Bayesian Model -- 3. A Crash Course in Bayesian Inference -- Part II. Estimation of Linearized DSGE Models -- 4. Metropolis-Hastings Algorithms for DSGE Models -- 5. Sequential Monte Carlo Methods -- 6. Three Applications -- Part III. Estimation of Nonlinear DSGE Models -- 7. From Linear to Nonlinear DSGE Models -- 8. Particle Filters -- 9. Combining Particle Filters with MH Samplers -- 10. Combining Particle Filters with SMC Samplers -- Appendix A. Model Descriptions -- Appendix B. Data Sources -- Bibliography -- Index
Summary: 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.
Holdings
Item type Current library Call number URL Status Notes Barcode
eBook eBook Biblioteca "Angelicum" Pont. Univ. S.Tommaso d'Aquino Nuvola online online - DeGruyter (Browse shelf(Opens below)) Online access Not for loan (Accesso limitato) Accesso per gli utenti autorizzati / Access for authorized users (dgr)9781400873739

Frontmatter -- Contents -- Figures -- Tables -- Series Editors' Introduction -- Preface -- Part I. Introduction to DSGE Modeling and Bayesian Inference -- 1. DSGE Modeling -- 2. Turning a DSGE Model into a Bayesian Model -- 3. A Crash Course in Bayesian Inference -- Part II. Estimation of Linearized DSGE Models -- 4. Metropolis-Hastings Algorithms for DSGE Models -- 5. Sequential Monte Carlo Methods -- 6. Three Applications -- Part III. Estimation of Nonlinear DSGE Models -- 7. From Linear to Nonlinear DSGE Models -- 8. Particle Filters -- 9. Combining Particle Filters with MH Samplers -- 10. Combining Particle Filters with SMC Samplers -- Appendix A. Model Descriptions -- Appendix B. Data Sources -- Bibliography -- Index

restricted access online access with authorization star

http://purl.org/coar/access_right/c_16ec

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.

Issued also in print.

Mode of access: Internet via World Wide Web.

In English.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021)