Bayesian estimation of DSGE models

Cover -- Title -- Copyright -- 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.