A Robust and Efficient Method for Solving Nonlinear Rational Expectations Models


WP/96/106-EA

A Robust and Efficient Method for Solving Nonlinear Rational
Expectations Models by Michel Jillard and Douglas Laxton


The development and use of forward-looking macro models in policymaking
institutions has proceeded at a pace much slower than predicted in the early
1980s. An important reason is that researchers have not had access to robust
and efficient solution techniques for solving nonlinear forward-looking
models. The numerical complexity of solving a forward-looking macro model is
considerably more onerous than solving a traditional backward-looking,
reduced-form model of the same size. In many cases, researchers have been
forced either to linearize their models or to focus their attention on very
small models that could be solved easily with available technology. This
paper discusses the development and implementation of a new algorithm based
on a Newton-Raphson iterative method. It is used for solving MULTIMOD, the
IMF's multicountry model of the world economy. This algorithm is
considerably faster and much less prone to simulation failure than
traditional algorithms and can also be used to solve individual country
models of the same size.