A RECURSIVE FORWARD SIMULATION METHOD FOR SOLVING NONLINEAR RATIONAL-EXPECTATIONS MODELS

This paper describes a recursive forward simulation method to solve dy namic stochastic optimization problems. The proposed simulation method uses a combination of nonlinear Euler equations and linearized decisi on rules to obtain numerical solution paths for the endogenous variabl es of the model. Diagnostic tests from 1000 repetitions for each of th e ten parameter configurations of a one-sector stochastic growth model indicate that forward simulation and standard simulation are about eq ually successful in generating a martingale Euler equation shock when the shock variance is small, but that forward simulation outperforms s tandard simulation when the shock variance is large.