Monetary policy in an uncertain world: probability models and the design of robust monetary rules

Abstract

PurposeThe purpose of this paper is to describe the transformation of macro‐modelling from reduced form behavioural equations estimated separately, through to contemporary microfounded dynamic stochastic general equilibrium (DSGE) models estimated by systems methods. It is argued that estimated DSGE models should be seen as probability models that can be used as a laboratory for assessing new policies in a new and uncertain environment. The methodology is particularly relevant for emerging economies such as India.Design/methodology/approachThis paper has analytical, empirical and policy dimensions. Estimating DSGE models by Bayesian‐Maximum‐Likelihood methods results in a posterior distribution of parameters that quantifies the uncertainty facing the policymaker. This, in turn, can be used for robust policy design.FindingsThe paper reviews evidence that inflation targeting in emerging economies welfare‐dominates exchange rate targeting.Originality/valueThis lies in the papers reviewed including those involving the author.