Risk Management in Action - Robust Monetary Policy Rules under Structured Uncertainty

Recent interest in 'Risk Management' has highlighted the relevance of Bayesian analysis for robust monetary-policy making. This paper sets out a comprehensive methodology for designing policy rules inspired by such considerations. We design rules that are robust with respect to model uncertainty facing both the policymaker and private sector. We apply our methodology to three simple interest-rate rules: inflation-forecast-based (IFB) rules with a discrete forward horizon, one targeting a discounted sum of forward inflation, and a current wage inflation rule. We use an estimated DSGE model of the euro area and estimated measures of structured exogenous and parameter uncertainty for the exercise. We find that IFB rules with a long horizon perform poorly with or without robust design. Our discounted future targeting rule performs much better, indicating that policy can be highly forward-looking without compromising stabilization. The wage inflation rule dominates whether it is designed to have good robust properties or not