Dynamic stochastic general equilibrium (dsge) modelling: Theory and practice

In recent years DSGE (dynamic stochastic general equilibrium) 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 VAR, or large-scale econometric models) are less a theoretic and with secure micro-foundations based on the optimizing behavior of rational economic agents. Apart from being "structural", the models bring out the key role of expectations and (being of a general equilibrium nature) can help the policy maker by explicitly projecting the macro - economic scenarios in response to various contemplated policy outcomes. 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 Macro -economics (NCM). Using the prototype real business cycle model as an illustration, this paper brings out the econometric structure underpinning such models. Estimation and inferential issues are discussed at length with a special emphasis on the role of Bayesian maximum likelihood methods. A detailed analytical critique is also presented together with some promising leads for future research.