A nonlinear certainty equivalent approximation method for dynamic stochastic problems

Abstract

This paper introduces a nonlinear certainty‐equivalent approximation method for dynamic stochastic problems. We first introduce a novel, stable, and efficient method for computing the decision rules in deterministic dynamic economic problems. We use the results as nonlinear and global certainty‐equivalent approximations for solutions to stochastic problems, and compare their accuracy to the common linear and local certainty‐equivalent methods. Our examples demonstrate that this method can be applied to solve high‐dimensional problems with up to 400 state variables with acceptable accuracy. This method can also be applied to solve problems with inequality constraints. These features make the nonlinear certainty‐equivalent approximation method suitable for solving complex economic problems, where other algorithms, such as log‐linearization, fail to produce a valid global approximation or are far less tractable. New Keynesian DSGE model competitive equilibrium parallel computing sparse grid approximation real business cycle model C61 C63 C68 E31 E52