TY  - BOOK
AU  - Herbst,Edward P.
AU  - Schorfheide,Frank
TI  - Bayesian Estimation of DSGE Models
T2  - The Econometric and Tinbergen Institutes Lectures
SN  - 9780691161082
AV  - HB145 .H467 2017
U1  - 339.501519542 23
PY  - 2015///]
CY  - Princeton, NJ : 
PB  - Princeton University Press, 
KW  - Bayesian statistical decision theory
KW  - Econometrics
KW  - Equilibrium (Economics)
KW  - Mathematical models
KW  - Equilibrium (Economics); Mathematical models
KW  - Stochastic analysis
KW  - BUSINESS & ECONOMICS / Econometrics
KW  - bisacsh
KW  - AR processes
KW  - Bayesian analysis
KW  - Bayesian estimation
KW  - Bayesian inference
KW  - Bayesian versions
KW  - DSGE model
KW  - DSGE models
KW  - Gaussian linear regression
KW  - Kalman filter
KW  - MCMC algorithms
KW  - MCMC methods
KW  - MH samplers
KW  - Monte Carlo method
KW  - New Keynesian DSGE model
KW  - New Keynesian model
KW  - PFMH algorithm
KW  - PMCMC
KW  - RMWHV algorithm
KW  - RWMH algorithm
KW  - SMC algorithm
KW  - SMC algorithms
KW  - VAR processes
KW  - algorithm
KW  - autoregressive model
KW  - bootstrap filter
KW  - calculus of probability
KW  - central bank
KW  - central banks
KW  - computation
KW  - dynamic stochastic general equilibrium
KW  - economy
KW  - equilibrium conditions
KW  - exogenous shock processes
KW  - exogenous shocks
KW  - filtering algorithm
KW  - fiscal policy
KW  - inflation
KW  - likelihood function
KW  - linearization techniques
KW  - log-linearization
KW  - macroeconomics
KW  - multimodal posteriors
KW  - nonlinear techniques
KW  - particle filter approximation
KW  - particle filtering methods
KW  - particle filters
KW  - policy analysis
KW  - posterior distribution
KW  - posterior distributions
KW  - posterior inference
KW  - posterior sampler
KW  - prior distribution
KW  - proposal distributions
KW  - random walk MH
KW  - stateгpace model
KW  - stateгpace models
KW  - stateгpace representation
N1  - 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; Issued also in print
N2  - 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
UR  - https://doi.org/10.1515/9781400873739?locatt=mode:legacy
UR  - https://www.degruyter.com/isbn/9781400873739
UR  - https://www.degruyter.com/cover/covers/9781400873739.jpg
ER  -