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A comparison of numerical methods for the solution of continuous-time DSGE models

  • Juan Carlos Parra-Alvarez

    ()

    (Aarhus University and CREATES)

This paper evaluates the accuracy of a set of techniques that approximate the solution of continuous-time DSGE models. Using the neoclassical growth model I compare linear-quadratic, perturbation and projection methods. All techniques are applied to the HJB equation and the optimality conditions that define the general equilibrium of the economy. Two cases are studied depending on whether a closed form solution is available. I also analyze how different degrees of non-linearities affect the approximated solution. The results encourage the use of perturbations for reasonable values of the structural parameters of the model and suggest the use of projection methods when a high degree of accuracy is required.

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File URL: ftp://ftp.econ.au.dk/creates/rp/13/rp13_39.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2013-39.

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Length: 35
Date of creation: 11 2013
Date of revision:
Handle: RePEc:aah:create:2013-39
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. John B. Taylor & Harald Uhlig, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," NBER Working Papers 3117, National Bureau of Economic Research, Inc.
  2. Jess Gaspar & Kenneth L. Judd, 1997. "Solving Large Scale Rational Expectations Models," NBER Technical Working Papers 0207, National Bureau of Economic Research, Inc.
  3. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2004. "Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood," PIER Working Paper Archive 04-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  4. Olaf Posch & Timo Trimborn, 2011. "Numerical Solution of Dynamic Equilibrium Models under Poisson Uncertainty," CESifo Working Paper Series 3431, CESifo Group Munich.
  5. Benigno, Pierpaolo & Woodford, Michael, 2012. "Linear-quadratic approximation of optimal policy problems," Journal of Economic Theory, Elsevier, vol. 147(1), pages 1-42.
  6. Tapiero, Charles S & Sulem, Agnes, 1994. "Computational Aspects in Applied Stochastic Control," Computational Economics, Society for Computational Economics, vol. 7(2), pages 109-46.
  7. Eric T. Swanson, 2012. "Risk Aversion and the Labor Margin in Dynamic Equilibrium Models," American Economic Review, American Economic Association, vol. 102(4), pages 1663-91, June.
  8. Collard, Fabrice & Juillard, Michel, 2001. "Accuracy of stochastic perturbation methods: The case of asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 979-999, June.
  9. Dario Caldara & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Yao Wen, 2012. "Computing DSGE models with recursive preferences and stochastic volatility," Finance and Economics Discussion Series 2012-04, Board of Governors of the Federal Reserve System (U.S.).
  10. Jessica Wachter, 2008. "Can time-varying risk of rare disasters explain aggregate stock market volatility?," 2008 Meeting Papers 944, Society for Economic Dynamics.
  11. Olivier Blanchard, 2009. "The State of Macro," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 209-228, 05.
  12. Den Haan, Wouter J & Marcet, Albert, 1994. "Accuracy in Simulations," Review of Economic Studies, Wiley Blackwell, vol. 61(1), pages 3-17, January.
  13. Ulrich Doraszelski & Kenneth L. Judd, 2012. "Avoiding the curse of dimensionality in dynamic stochastic games," Quantitative Economics, Econometric Society, vol. 3(1), pages 53-93, 03.
  14. Fransesco Furlanetto & Martin Seneca, 2010. "New Perspectives on Depreciation Shocks as a Source of Business Cycle Fluctuations," Economics wp48, Department of Economics, Central bank of Iceland.
  15. Olaf Posch, 2007. "Structural estimation of jump-diffusion processes in macroeconomics," CREATES Research Papers 2007-23, School of Economics and Management, University of Aarhus.
  16. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-70, November.
  17. Tom Kompas & Long Chu, 2010. "A Comparison of Parametric Approximation Techniques to Continuous-Time Stochastic Dynamic Programming Problems," Environmental Economics Research Hub Research Reports 1071, Environmental Economics Research Hub, Crawford School of Public Policy, The Australian National University.
  18. Willi Semmler & Stephanie Becker & Lars Gruene, 2006. "Comparing Accuracy of Second Order Approximation and Dynamic Programming," Computing in Economics and Finance 2006 469, Society for Computational Economics.
  19. Magill, Michael J. P., 1977. "A local analysis of N-sector capital accumulation under uncertainty," Journal of Economic Theory, Elsevier, vol. 15(1), pages 211-219, June.
  20. Stephen Turnovsky & William Smith, 2004. "Equilibrium Consumption and Precautionary Savings in a Stochastically Growing Economy," Working Papers UWEC-2006-01-P, University of Washington, Department of Economics, revised Oct 2004.
  21. repec:cup:cbooks:9780521541947 is not listed on IDEAS
  22. repec:cup:cbooks:9780521291873 is not listed on IDEAS
  23. Ralph S.J. Koijen & Jules H. van Binsbergen & Juan F. Rubio-Ramírez & Jesus Fernandez-Villaverde, 2008. "Likelihood Estimation of DSGE Models with Epstein-Zin Preferences," 2008 Meeting Papers 1099, Society for Economic Dynamics.
  24. Klaus Wälde, 2009. "Production Technologies in Stochastic Continuous Time Models," CESifo Working Paper Series 2831, CESifo Group Munich.
  25. Yihong Xia, 2001. "Learning about Predictability: The Effects of Parameter Uncertainty on Dynamic Asset Allocation," Journal of Finance, American Finance Association, vol. 56(1), pages 205-246, 02.
  26. Lars Ljungqvist & Thomas J. Sargent, 2004. "Recursive Macroeconomic Theory, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026212274x, June.
  27. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-57, August.
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