Negotiating the wilderness of bounded rationality through robust policy

We show how the “wilderness of non-rationality” posed for the policymaker may be negotiated by designing a robust Taylor-type monetary rule across a RE NK model and competing behavioral alternatives. The latter consists of a model with “Euler learning” and a bounded rational one with myopia. For the former, expectations of endogenous variables take the form of a general heuristic rule, supported by an experiment study, that encompasses simple adaptive expectations. In both heuristic rules, there is a version with lagged observables giving potentially six competing NK models. In our novel forward-looking approach, policymakers weight models based on relative forecasting performance rather than Bayesian model averaging. We find that the robust optimized rule found using optimal pooling weights is very close to the price-level rule.