Download now Free registration required
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. The authors design rules that are robust with respect to model uncertainty facing both the policymaker and private sector. They apply the 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. They use an estimated DSGE model of the euro area and estimated measures of structured exogenous and parameter uncertainty for the exercise. They find that IFB rules with a long horizon perform poorly with or without robust design.
- Format: PDF
- Size: 926.1 KB