IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-07c60003.html
   My bibliography  Save this article

On the accuracy of low-order projection methods

Author

Listed:
  • Paul Pichler

    (University of Vienna)

Abstract

We use low-order projection methods to compute numerical solutions of the basic neoclassical stochastic growth model. We assess the quality of the obtained solutions, and compare them to numerical approximations derived with first and second-order perturbation techniques. We show that projection methods perform surprisingly poor when the degree of approximation is very low, and we provide some intuition behind this finding.

Suggested Citation

  • Paul Pichler, 2007. "On the accuracy of low-order projection methods," Economics Bulletin, AccessEcon, vol. 3(50), pages 1-8.
    • Handle: RePEc:ebl:ecbull:eb-07c60003
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/pubs/EB/2007/Volume3/EB-07C60003A.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
    2. Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
    3. Christiano, Lawrence J. & Fisher, Jonas D. M., 2000. "Algorithms for solving dynamic models with occasionally binding constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 24(8), pages 1179-1232, July.
    4. Alfred Maussner, 2005. "Projection Methods (GAUSS)," QM&RBC Codes 135, Quantitative Macroeconomics & Real Business Cycles.
    5. Dueker Michael & Fischer Andreas & Dittmar Robert, 2007. "Stochastic Capital Depreciation and the Co-movement of Hours and Productivity," The B.E. Journal of Macroeconomics, De Gruyter, vol. 6(3), pages 1-24, January.
    6. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
    7. Larry E. Jones & Rodolfo E. Manuelli & Henry E. Siu, 2005. "Fluctuations in Convex Models of Endogenous Growth II: Business Cycle Properties," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(4), pages 805-828, October.
    8. Radim Bohacek & Michal Kejak, 2005. "Projection Methods for Economies with Heterogeneous Agents," CERGE-EI Working Papers wp258, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Gapen Michael T. & Cosimano Thomas F., 2005. "Solving Ramsey Problems with Nonlinear Projection Methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    10. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
    11. Siu, Henry E., 2004. "Optimal fiscal and monetary policy with sticky prices," Journal of Monetary Economics, Elsevier, vol. 51(3), pages 575-607, April.
    12. Paul Pichler, 2005. "Evaluating Approximate Equilibria of Dynamic Economic Models," Vienna Economics Papers 0510, University of Vienna, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:ebl:ecbull:v:3:y:2007:i:50:p:1-8 is not listed on IDEAS
    2. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    3. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
    4. Posch, Olaf & Trimborn, Timo, 2013. "Numerical solution of dynamic equilibrium models under Poisson uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2602-2622.
    5. Serguei Maliar & John Taylor & Lilia Maliar, 2016. "The Impact of Alternative Transitions to Normalized Monetary Policy," 2016 Meeting Papers 794, Society for Economic Dynamics.
    6. Francisco (F.) Blasques & Marc Nientker, 2019. "Transformed Perturbation Solutions for Dynamic Stochastic General Equilibrium Models," Tinbergen Institute Discussion Papers 19-012/III, Tinbergen Institute, revised 09 Feb 2020.
    7. 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.
    8. Paul Pichler, 2005. "Evaluating Approximate Equilibria of Dynamic Economic Models," Vienna Economics Papers 0510, University of Vienna, Department of Economics.
    9. S. Sirakaya & Stephen Turnovsky & M. Alemdar, 2006. "Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 185-206, May.
    10. Guerrieri, Luca & Iacoviello, Matteo, 2015. "OccBin: A toolkit for solving dynamic models with occasionally binding constraints easily," Journal of Monetary Economics, Elsevier, vol. 70(C), pages 22-38.
    11. Stephanie Becker & Lars Grüne & Willi Semmler, 2007. "Comparing accuracy of second-order approximation and dynamic programming," Computational Economics, Springer;Society for Computational Economics, vol. 30(1), pages 65-91, August.
    12. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2014. "Lower Bounds on Approximation Errors: Testing the Hypothesis That a Numerical Solution Is Accurate?," BYU Macroeconomics and Computational Laboratory Working Paper Series 2014-06, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
    13. José Cao-Alvira, 2010. "Finite Elements in the Presence of Occasionally Binding Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 35(4), pages 355-370, April.
    14. Michel Juillard & Tarik Ocaktan, 2008. "Méthodes de simulation des modèles stochastiques d'équilibre général," Economie & Prévision, La Documentation Française, vol. 0(2), pages 115-126.
    15. Robert Amano & Malik Shukayev, 2012. "Risk Premium Shocks and the Zero Bound on Nominal Interest Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1475-1505, December.
    16. Evans, Martin D.D. & Hnatkovska, Viktoria, 2012. "A method for solving general equilibrium models with incomplete markets and many financial assets," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1909-1930.
    17. Dorofeenko, Victor & Lee, Gabriel S. & Salyer, Kevin D., 2010. "A new algorithm for solving dynamic stochastic macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 388-403, March.
    18. Ayşe Kabukçuoğlu & Enrique Martínez-García, 2021. "A Generalized Time Iteration Method for Solving Dynamic Optimization Problems with Occasionally Binding Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 435-460, August.
    19. Heiberger, Christopher & Maußner, Alfred, 2020. "Perturbation solution and welfare costs of business cycles in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    20. Manuel S. Santos & Adrian Peralta-Alva, 2012. "Analysis of Numerical Errors," Working Papers 2012-6, University of Miami, Department of Economics.
    21. Martin D. D. Evans & Viktoria Hnatkovska, 2005. "Solving General Equilibrium Models with Incomplete Markets and Many Assets," NBER Technical Working Papers 0318, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    numerical accuracy;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • E0 - Macroeconomics and Monetary Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ebl:ecbull:eb-07c60003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: John P. Conley (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.