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
Statistical Papers – Springer Journals
Published: Jul 10, 2017
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