Computing second-order-accurate solutions for rational expectation models using linear solution methods

This paper shows how to compute a second-order accurate solution of a non-linear rational expectation model using algorithms developed for the solution of linear rational expectation models. The result is a state - space representation for the realized values of the variables of the model. This state - space representation can easily be used to compute impulse responses as well as conditional and unconditional forecasts.