Bayesian Estimation of DSGE Models

✍ Scribed by Edward P. Herbst; Frank Schorfheide

• Publisher: Princeton University Press
• Year: 2015
• Tongue: English
• Leaves: 295
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Contents
Figures
Tables
Series Editors’ Introduction
Preface
I.
Introduction to DSGE Modeling and Bayesian Inference
1.
DSGE Modeling
1.1 A Small-Scale New Keynesian DSGE Model
1.2 Other DSGE Models Considered in This Book
2.
Turning a DSGE Model into a Bayesian Model
2.1 Solving a (Linearized) DSGE Model
2.2 The Likelihood Function
2.3 Priors
3.
A Crash Course in Bayesian Inference
3.1 The Posterior of a Linear Gaussian Model
3.2 Bayesian Inference and Decision Making
3.3 A Non-Gaussian Posterior
3.4 Importance Sampling
3.5 Metropolis-Hastings Algorithms
II.
Estimation of Linearized DSGE Models
4.
Metropolis-Hastings Algorithms for DSGE Models
4.1 A Benchmark Algorithm
4.2 The RWMH-V Algorithm at Work
4.3 Challenges Due to Irregular Posteriors
4.4 Alternative MH Samplers
4.5 Comparing the Accuracy of MH Algorithms
4.6 Evaluation of the Marginal Data Density
5.
Sequential Monte Carlo Methods
5.1 A Generic SMC Algorithm
5.2 Further Details of the SMC Algorithm
5.3 SMC for the Small-Scale DSGE Model
6.
Three Applications
6.1 A Model with Correlated Shocks
6.2 The Smets-Wouters Model with a Diffuse Prior
6.3 The Leeper-Plante-Traum Fiscal Policy Model
III.
Estimation of Nonlinear DSGE Models
7.
From Linear to Nonlinear DSGE Models
7.1 Nonlinear DSGE Model Solutions
7.2 Adding Nonlinear Features to DSGE Models
8.
Particle Filters
8.1 The Bootstrap Particle Filter
8.2 A Generic Particle Filter
8.3 Adapting the Generic Filter
8.4 Additional Implementation Issues
8.5 Adapting st–1 Draws
8.6 Application to the Small-Scale DSGE Model
8.7 Application to the SW Model
8.8 Computational Considerations
9.
Combining Particle Filters with MH Samplers
9.1 The PFMH Algorithm
9.2 Application to the Small-Scale DSGE Model
9.3 Application to the SW Model
9.4 Computational Considerations
10.
Combining Particle Filters with SMC Samplers
10.1 An SMC
2 Algorithm
10.2 Application to the Small-Scale DSGE Model
10.3 Computational Considerations
Appendix
A.
Model Descriptions
A.1 Smets-Wouters Model
A.2 Leeper-Plante-Traum Fiscal Policy Model
B.
Data Sources
B.1 Small-Scale New Keynesian DSGE Model
B.2 Smets-Wouters Model
B.3 Leeper-Plante-Traum Fiscal Policy Model
Bibliography
Index