We urge the use of Structural Dynamic Factor Models (DFM) to validate and to guide the construction of Dynamic Stochastic General Equilibrium (DSGE) models. The main reason is that the log-linear solution of a DSGE model has a factor structure which ensures consistency between the representations of the two models. We assess, by means of a few simulations, the validity of SDFM as an empirical tool to complement DSGE analysis. Using a DSGE model as data generating process, the factor model provides very accurate estimates of the true impulse response functions. As an application, we validate a theory of TFP news and surprise shocks.

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.