Dynamic stochastic general equilibrium (DSGE) modelling in practice: identification, estimation and evaluation

In recent years, dynamic stochastic general equilibrium (DSGE) models have come to play an increasing role in central banks, as an aid in the formulation of monetary policy (and increasingly after the global crisis, for maintaining financial stability). DSGE models, compared to other widely prevalent econometric models (such as vector autoregressive or large-scale econometric models), are less a-theoretic and with secure micro-foundations based on the optimizing behaviour of rational economic agents. Additionally, the models in spite of being strongly tied to theory, can be ‘taken to the data’ in a meaningful way. A major feature of these models is that their theoretical underpinnings lie in what has now come to be called as the New Consensus Macroeconomics (NCM). This paper concentrates on the econometric structure underpinning such models. Identification, estimation and evaluation issues are discussed at length with a special emphasis on the role of Bayesian maximum likelihood methods.