Solving SDGE Models: Approximation About The Stochastic Steady State

Michel Juillard and Ondra Kamenik

No 106, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: Recently, there has been many applications of perturbation methods for solving stochastic dynamic general equilibrium models. However, in standard applications of the perturbation method, the Taylor expansion is always computed around the deterministic steady state. Because of nonlinearities, the center of the ergodic distribution of the endogenous variables can be away from the deterministic steady state, making it not the best point around which to take the approximation. In this paper, we advocate the computation of the approximation around the stochastic steady state. We define the stochastic steady state as the point of the state space where, in absence of shocks in that period, agents would choose to remain although that there are taking into account future volatility. The paper suggests a simple and practical way of calculating the approximation at the stochastic steady state. The proposed method, instead of doing one big leap toward uncertainty, makes a few smaller steps adding a new portion of uncertainity in each. Each step corresponds to a problem of finding an approximation of the decision rule with smaller shocks than in the original problem; hence the method is practical, since it allows using an existing implementation of perturbation method. The paper provides results on a few example models using a framework of Dynare++.

JEL-codes: C61 C63 E37 (search for similar items in EconPapers)
Date: 2005-11-11
References: Add references at CitEc
Citations: View citations in EconPapers (23)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:106

Access Statistics for this paper

More papers in Computing in Economics and Finance 2005 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().