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Computing DSGE Models with Recursive Preferences

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

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.

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  • Dario Caldara & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Wen Yao, 2009. "Computing DSGE Models with Recursive Preferences," NBER Working Papers 15026, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:15026
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    7. Francisco Ruge‐Murcia, 2017. "Skewness Risk and Bond Prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 379-400, March.
    8. 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.
    9. Ales Marsal & Lorant Kaszab & Roman Horvath, 2017. "Government Spending and the Term Structure of Interest Rates in a DSGE Model," Working and Discussion Papers WP 3/2017, Research Department, National Bank of Slovakia.
    10. Posch, Olaf & Trimborn, Timo, 2010. "Numerical solution of continuous-time DSGE models under Poisson uncertainty," Hannover Economic Papers (HEP) dp-450, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. Maximiliano Dvorkin, 2017. "Skills, Occupations, and the Allocation of Talent over the Business Cycle," 2017 Meeting Papers 1527, Society for Economic Dynamics.
    12. Leland E. Farmer & Alexis Akira Toda, 2017. "Discretizing nonlinear, non‐Gaussian Markov processes with exact conditional moments," Quantitative Economics, Econometric Society, vol. 8(2), pages 651-683, July.
    13. 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.
    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. 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.
    16. 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.
    17. 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.
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    19. Baltasar Manzano & Luis Rey, 2012. "The Welfare Cost of Energy Insecurity," Working Papers fa07-2012, Economics for Energy.
    20. 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.

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    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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