Methods for robust control

Abstract

Robust control allows policymakers to formulate policies that guard against model misspecification. The principal tools used to solve robust control problems are state-space methods [see Hansen, L.P., Sargent T.J., 2008. Robustness. Princeton University Press; Giordani, P., Söderlind, P., 2004. Solution of macromodels with Hansen–Sargent robust policies: some extensions. Journal of Economic Dynamics and Control 28 (12), 2367–2397]. In this paper we show that the structural-form methods developed by Dennis [2007. Optimal policy rules in rational-expectations models: new solution algorithms. Macroeconomic Dynamics 11 (1), 31–55] to solve control problems with rational expectations can also be applied to robust control problems, with the advantage that they bypass the task, often onerous, of having to express the reference model in state-space form. In addition, we show how to implement two different timing assumptions with distinct implications for the robust policy and the economy. We apply our methods to a New Keynesian Dynamic Stochastic General Equilibrium model and find that robustness has important effects on policy and the economy.