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

Edward P. Herbst, Frank Schorfheide, Bayesian Estimation of DSGE Models Stat Papers (2017) 58:955–956 DOI 10.1007/s00362-017-0934-4 BOOK REVIEW Edward P. Herbst, Frank Schorfheide, Bayesian Estimation of DSGE Models Series: The Econometric and Tinbergen Institutes Lectures, eds. Dennis Fok and Philip Hans Franses. Princeton University Press, 2016, xx + 296 pp., $ 49.50, ISBN 978-0691161082 Peter Hackl Received: 29 June 2017 / Revised: 29 June 2017 / Published online: 10 July 2017 © Springer-Verlag GmbH Germany 2017 Dynamic stochastic general equilibrium (DSGE) models have become a standard framework for modelling in modern macroeconomics; they are extensively used for policy analysis at central banks and in academic research. This book surveys stan- dard and advanced computational techniques used in the Bayesian analysis of DSGE models. It has three parts: The first contains an introduction to DSGE modelling, start- ing with a small-scale New Keynesian DSGE model and sketching the well-known Smets-Wouters model, and transforming the log-linearized DSGE model into its state- space representation. Also contained is a “crash course in Bayesian inference”, with sections on computational techniques such as importance sampling and the Metropolis- Hastings (MH) algorithm, both with numerical illustrations. The second part covers Bayesian computations for linearized DSGE models with Gaussian shocks, using the Kalman filter for the evaluation http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistical Papers Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Statistics; Statistics for Business/Economics/Mathematical Finance/Insurance; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods; Operations Research/Decision Theory
ISSN
0932-5026
eISSN
1613-9798
D.O.I.
10.1007/s00362-017-0934-4
Publisher site
See Article on Publisher Site

Abstract

Stat Papers (2017) 58:955–956 DOI 10.1007/s00362-017-0934-4 BOOK REVIEW Edward P. Herbst, Frank Schorfheide, Bayesian Estimation of DSGE Models Series: The Econometric and Tinbergen Institutes Lectures, eds. Dennis Fok and Philip Hans Franses. Princeton University Press, 2016, xx + 296 pp., $ 49.50, ISBN 978-0691161082 Peter Hackl Received: 29 June 2017 / Revised: 29 June 2017 / Published online: 10 July 2017 © Springer-Verlag GmbH Germany 2017 Dynamic stochastic general equilibrium (DSGE) models have become a standard framework for modelling in modern macroeconomics; they are extensively used for policy analysis at central banks and in academic research. This book surveys stan- dard and advanced computational techniques used in the Bayesian analysis of DSGE models. It has three parts: The first contains an introduction to DSGE modelling, start- ing with a small-scale New Keynesian DSGE model and sketching the well-known Smets-Wouters model, and transforming the log-linearized DSGE model into its state- space representation. Also contained is a “crash course in Bayesian inference”, with sections on computational techniques such as importance sampling and the Metropolis- Hastings (MH) algorithm, both with numerical illustrations. The second part covers Bayesian computations for linearized DSGE models with Gaussian shocks, using the Kalman filter for the evaluation

Journal

Statistical PapersSpringer Journals

Published: Jul 10, 2017

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