Solving Large Scale Rational Expectations Models

Abstract

We explore alternative approaches to numerical solutions of large rational expectations models. We discuss and compare several current alternatives, focussing on the tradeoffs in accuracy, space, and speed. The models range from representative agent models with many goods and capital stocks, to models of heterogeneous agents with complete or incomplete asset markets. The methods discussed include perturbation and projection methods. We show that these methods are capable of analyzing moderately large models even when we use only elementary, general purpose numerical methods.