Designing Optimal Macroeconomic Policy Rules Under Parameter Uncertainty: A Stochastic Dominance Approach

Abstract

This paper offers a Bayesian decision-theoretic approach to policy evaluation in rational expectation models. First, we show how to correctly assess and rank simple policy rules under the welfare loss minimization criterion in the presence of uncertainty about the model’s structural parameters. We consider a Bayesian policymaker who assesses the effectiveness of policy actions by comparing the distributions of welfare losses using stochastic dominance orderings. Second, we propose a new Bayesian testing procedure for verifying the k-degree stochastic dominance relation. Third, we apply our approach to a dynamic stochastic general equilibrium model, estimated for the U.S. economy. We show that using stochastic dominance to rank simple policy rules yields different rankings than using well-established robust approaches. The contemporaneous monetary policy rule that reacts to inflation and the output gap, with an interest rate smoothing mechanism, minimises the welfare loss for all decision-makers who admit infinite degree stochastic dominance preferences.