Working Paper: NBER ID: w18983
Authors: Martin M. Andreasen; Jess Fernández-Villaverde; Juan Rubio-Ramirez
Abstract: This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for first and second unconditional moments and impulse response functions. Thus, our analysis introduces GMM estimation for DSGE models approximated up to third-order and provides the foundation for indirect inference and SMM when simulation is required. We illustrate the usefulness of our approach by estimating a New Keynesian model with habits and Epstein-Zin preferences by GMM when using first and second unconditional moments of macroeconomic and financial data and by SMM when using additional third and fourth unconditional moments and non-Gaussian innovations.
Keywords: DSGE Models; Pruning Method; GMM Estimation; SMM; Higher-Order Approximations
JEL Codes: C15; C53; E30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| pruning method (C24) | prevents explosive sample paths (C69) |
| pruning method (C24) | maintains stability (C62) |
| pruning method (C24) | existence of finite first and second unconditional moments (C46) |
| pruning method (C24) | accurate estimation of New Keynesian model (E12) |
| pruning method (C24) | improved model fit to empirical data (C52) |