Higher-Order Solutions to Dynamic, Discrete-Time Rational Expectations Models: Methods and an Application to Optimal Monetary Policy

We present an algorithm and software routines for computing nth-order approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment (and from a ``timeless perspective'') in an optimizing-agent model with nominal price rigidities, subject to a fiscal policy that is stochastic, suboptimal, and exogenous to the central bank