Evidence-Based Policy Choice in Macroeconomics

This paper studies data-driven macroeconomic policy choice in a structural VAR framework. We formulate the policymaker's problem of optimizing the future path of a policy instrument as a statistical decision problem under two alternative policy regimes: a "contemporaneous" regime, where the chosen path is implemented through unexpected policy shocks each period, and a "forward guidance" regime, where the expected path is announced in advance and enforced through anticipated policy shocks. We derive oracle, Bayes-optimal, and robust Bayes decision rules, the last for the case where structural impulse responses are only set-identified, and show that the decision problem depends on the structural rotation matrix only through the span of the columns associated with the policy shocks. We argue that the plug-in rule is uniformly dominated by a James-Stein-type shrinkage rule in a local asymptotic Gaussian limit experiment, and is therefore inadmissible therein. Numerical analysis calibrated to monetary SVARs compares the risks of the plug-in and a benchmark Bayes rule in Monte Carlo Gaussian simulations and finds that the Bayes rule attains lower risk at 95.1-99.9 percent of the posterior draws in the two regimes. We then study the relationship between the one-shot decision for a policy path and sequential policymaking, and show that, in the contemporaneous regime, the one-shot oracle path coincides with the expectation of the sequentially optimal oracle plan; we document that this representativeness property fails under Bayesian decision-making with parameter uncertainty. In an application to U.S. monetary policy in 2006-2007, we find that the plug-in and Bayes rules can differ substantially: the Bayes-optimal rule prescribes an initial tightening followed by partial easing across different specifications, while the plug-in changes across regimes.