Working Paper: CEPR ID: DP10999
Authors: Wouter den Haan; Michal L. Kobielarz; Pontus Rendahl
Abstract: This paper proposes an algorithm that finds model solutions at a particular point in the state space by solving a simple system of equations. The key step is to characterize future behavior with a Taylor series expansion of the current period's behavior around the contemporaneous values for the state variables. Since current decisions are solved from the original model equations, the solution incorporates nonlinearities and uncertainty. The algorithm is used to solve the model considered in Coeurdacier, Rey, and Winant (2011), which is a challenging model because it has no steady state and uncertainty is necessary to keep the model well behaved. We show that our algorithm can generate accurate solutions even when the model series are quite volatile. The solutions generated by the risky-steady-state algorithm proposed in Coeurdacier, Rey, and Winant (2011), in contrast, is shown to be not accurate.
Keywords: risky steady state; solution methods
JEL Codes: C63; E10; E23; F41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| exacttoday (et) algorithm (C51) | accurate solutions for stochastic dynamic models (C69) |
| exacttoday (et) algorithm (C51) | more accurate results than risky steady state algorithm (C62) |
| perturbation methods (C60) | inaccuracies in determining current period outcomes (G41) |
| exacttoday (et) algorithm (C51) | precise characterization of future behavior (D84) |
| exacttoday (et) algorithm (C51) | circumvent curse of dimensionality (C24) |
| exacttoday (et) algorithm (C51) | aligns with accurate projections in wealth and consumption outcomes (F62) |