| Abstract: |
This paper compares different solution methods for computing the equilibrium
of dynamic stochastic general equilibrium (DSGE) models with recursive
preferences such as those in Epstein and Zin (1989 and 1991) and stochastic
volatility. Models with these two features have recently become popular, but
we know little about the best ways to implement them numerically. To fill this
gap, we solve the stochastic neoclassical growth model with recursive
preferences and stochastic volatility using four different approaches: second-
and third-order perturbation, Chebyshev polynomials, and value function
iteration. We document the performance of the methods in terms of computing
time, implementation complexity, and accuracy. Our main finding is that
perturbations are competitive in terms of accuracy with Chebyshev polynomials
and value function iteration while being several orders of magnitude faster to
run. Therefore, we conclude that perturbation methods are an attractive
approach for computing this class of problems. |