We explore alternative approaches to numerical solutions of
large rational-expectations models. We discuss and compare several
current alternatives, focusing on the trade-offs 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 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.
