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Computing DSGE models with recursive preferences and stochastic volatility

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  • Dario Caldara
  • Jesús Fernández-Villaverde
  • Juan F. Rubio-Ramírez
  • Yao Wen

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.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2012-04.

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Date of creation: 2012
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Handle: RePEc:fip:fedgfe:2012-04

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Citations

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Cited by:
  1. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian Model: a formal test of backward- and forward-looking expectations," MPRA Paper 40278, University Library of Munich, Germany.
  2. Gospodinov, Nikolay & Lkhagvasuren, Damba, 2013. "A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains," Working Paper 2013-05, Federal Reserve Bank of Atlanta.
  3. Juan Carlos Parra-Alvarez, 2013. "A comparison of numerical methods for the solution of continuous-time DSGE models," CREATES Research Papers 2013-39, School of Economics and Management, University of Aarhus.
  4. Matthew Smith & Rhys Bidder, 2013. "Robust Animal Spirits," 2013 Meeting Papers 265, Society for Economic Dynamics.
  5. Martin Schneider & Cosmin Ilut & Francesco Bianchi, 2013. "Uncertainty Shocks, Asset Supply and Pricing over the Business Cycle," 2013 Meeting Papers 202, Society for Economic Dynamics.
  6. Neil Shephard, 2013. "Martingale unobserved component models," Economics Series Working Papers 644, University of Oxford, Department of Economics.
  7. Jules H. van Binsbergen & Jesús Fernández-Villaverde & Ralph S.J. Koijen & Juan F. Rubio-Ramírez, 2010. "The Term Structure of Interest Rates in a DSGE Model with Recursive Preferences," PIER Working Paper Archive 10-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  8. Mennuni, Alessandro, 2013. "Labor Force Composition and Aggregate Fluctuations," Discussion Paper Series In Economics And Econometrics 1302, Economics Division, School of Social Sciences, University of Southampton.
  9. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian model: A formal test of backward- and forward-looking behavior," Economics Working Papers 2012-07, Christian-Albrechts-University of Kiel, Department of Economics.
  10. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian Model: a formal test of backward- and forward-looking expectations," MPRA Paper 39669, University Library of Munich, Germany.
  11. Echevarría Olave, Cruz Angel & Iza Padilla, María Amaya, . "Income Taxation and Growth in an OLG Economy: Does Aggregate Uncertainty Play any Role?," DFAEII Working Papers DFAEII;2013-06, University of the Basque Country - Department of Foundations of Economic Analysis II.
  12. Cesa-Bianchi, Ambrogio & Fernandez-Corugedo, Emilio, 2014. "Uncertainty in a model with credit frictions," Bank of England working papers 496, Bank of England.

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