Nested models and model uncertainty

Uncertainty about the appropriate choice among nested models is a concern for optimal policy when policy prescriptions from those models differ. The standard procedure is to specify a prior over the parameter space ignoring the special status of sub-models, e.g., those resulting from zero restrictions. Following Sims(2008), we treat nested sub-models as ``probability models'' and we formalize a procedure that ensures that sub-models are not discarded too easily and do matter for optimal policy. For the United States, we find that optimal policy based on our procedure leads to substantial welfare gains compared to the standard procedure.