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Computing DSGE Models with Recursive Preferences

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  • Dario Caldara

    () (Institute of International Economic Studies, Stockholm University)

  • Jesus Fernandez-Villaverde

    () (Department of Economics, University of Pennsylvania)

  • Juan F. Rubio-Ramirez

    () (Department of Economics, Duke University)

  • Wen Yao

    () (Department of Economics, University of Pennsylvania)

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). Models with these preferences 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 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 a third-order perturbation is competitive in terms of accuracy with Chebyshev polynomials and value function iteration, while being an order of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.

Suggested Citation

  • Dario Caldara & Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez & Wen Yao, 2009. "Computing DSGE Models with Recursive Preferences," PIER Working Paper Archive 09-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Handle: RePEc:pen:papers:09-018
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    Cited by:

    1. Darracq Pariès, Matthieu & Loublier, Alexis, 2010. "Epstein-Zin preferences and their use in macro-finance models: implications for optimal monetary policy," Working Paper Series 1209, European Central Bank.
    2. Olaf Posch & Timo Trimborn, 2010. "Numerical solution of continuous-time DSGE models under Poisson uncertainty," Economics Working Papers 2010-08, Department of Economics and Business Economics, Aarhus University.
    3. François Gourio, 2013. "Credit Risk and Disaster Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(3), pages 1-34, July.
    4. Angelo M. Fasolo, 2011. "The Accuracy of Perturbation Methods to Solve Small Open Economy Models," Working Papers Series 262, Central Bank of Brazil, Research Department.
    5. Harold L. Cole & Lee E. Ohanian, 2013. "The Impact of Cartelization, Money, and Productivity Shocks on the International Great Depression," NBER Working Papers 18823, National Bureau of Economic Research, Inc.
    6. Boons, Martijn & Duarte, Fernando M. & de Roon, Frans & Szymanowska, Marta, 2013. "Time-varying inflation risk and the cross section of stock returns," Staff Reports 621, Federal Reserve Bank of New York, revised 01 Nov 2017.
    7. Andreasen , Martin & Zabczyk, Pawel, 2011. "An efficient method of computing higher-order bond price perturbation approximations," Bank of England working papers 416, Bank of England.
    8. van Binsbergen, Jules H. & Fernández-Villaverde, Jesús & Koijen, Ralph S.J. & Rubio-Ramírez, Juan, 2012. "The term structure of interest rates in a DSGE model with recursive preferences," Journal of Monetary Economics, Elsevier, vol. 59(7), pages 634-648.
    9. Backus, David & Ferriere, Axelle & Zin, Stanley, 2015. "Risk and ambiguity in models of business cycles," Journal of Monetary Economics, Elsevier, vol. 69(C), pages 42-63.
    10. Sanjay Chugh, 2016. "Firm Risk and Leverage-Based Business Cycles," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 111-131, April.
    11. Francois Gourio, 2012. "Disaster Risk and Business Cycles," American Economic Review, American Economic Association, vol. 102(6), pages 2734-2766, October.
    12. Hong Lan & Alexander Meyer-Gohde, 2013. "Pruning in Perturbation DSGE Models - Guidance from Nonlinear Moving Average Approximations," SFB 649 Discussion Papers SFB649DP2013-024, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Baltasar Manzano & Luis Rey, 2012. "The Welfare Cost of Energy Insecurity," Working Papers fa07-2012, Economics for Energy.
    14. Hong Lan & Alexander Meyer-Gohde, 2013. "Decomposing Risk in Dynamic Stochastic General Equilibrium," SFB 649 Discussion Papers SFB649DP2013-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Echevarría, Cruz A., 2012. "Income tax progressivity, physical capital, aggregate uncertainty and long-run growth in an OLG economy," Journal of Macroeconomics, Elsevier, vol. 34(4), pages 955-974.

    More about this item

    Keywords

    DSGE Models; Recursive Preferences; Perturbation;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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