Computing DSGE models with recursive preferences and stochastic volatility☆
Highlights
► We compare different computational methods for solving DSGE models with Epstein–Zin preferences and stochastic volatility. ► Projection delivers high accuracy but it is slow. ► Perturbation delivers more than acceptable accuracy at great speed. ► Value function iteration requires an inordinate amount of time to deliver an acceptable level of accuracy. ► Hence, we conclude that projection and perturbation methods are the best choices to handle this class of problems.
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Cited by (0)
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We thank Michel Juillard for his help with computational issues and Larry Christiano, Dirk Krueger, Pawel Zabczyk, and participants at several seminars for comments. Beyond the usual disclaimer, we must note that any views expressed herein are those of the authors and not necessarily those of the Board of Governors of the Federal Reserve System or the Federal Reserve Bank of Atlanta. Finally, we also thank the NSF for financial support.