Bayesian Estimation of DSGE Models

by Edward P. Herbst, Frank Schorfheide

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.

Genres: Economics

296 pages, Hardcover

First published January 26, 2016

Reviews

[Max Lybbert · Rating 4 out of 5]

The discussion of DSGE models is detailed, easy to follow, and well-written. I’ve had a list of papers to read that I hoped would explain the DSGE approach. This book took their place.

On the other hand, while the discussion about Bayesian methods is extensive and well-written, it isn’t targeted at the novice. The book recommends that anybody interested in Bayesian approaches use another book as an introduction. This decision is definitely understandable, but it’s also important information for potential readers.

[Richard Marney · Rating 4 out of 5]

Selected review. A useful reference source.