Bayesian Estimation of DSGE Models
Edward P Herbst author Frank Schorfheide author
Format:Hardback
Publisher:Princeton University Press
Published:22nd Jan '16
Should be back in stock very soon
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.
"Well written and well organized, and the topic analyzed is very interesting and current."--Manuel Salvador, MathSciNet
ISBN: 9780691161082
Dimensions: unknown
Weight: 454g
296 pages