IDEAS home Printed from https://ideas.repec.org/p/sce/scecfa/469.html
   My bibliography  Save this paper

Comparing Accuracy of Second Order Approximation and Dynamic Programming

Author

Listed:
  • Willi Semmler

    () (Economics New School University and CEM Bielefeld)

  • Stephanie Becker

    (University of Bayreuth)

  • Lars Gruene

    (University of Bayreuth)

Abstract

The accuracy of the solution of dynamic general equilibrium models has become a major issue. Recent papers, substituting second order for first order approximations, have shown to obtain significant differences in accuracy. Second order approximations have had some considerable success in solving the policy function of small as well as large scale models. Yet, the issue of accuracy is also relevant for the approximate solution of the value function. In numerous dynamic decision problems welfare needs to be computed through the approximation of the value function. Kim and Kim (2003), for example, find a reversal of welfare ordering by moving from first to second order approximations. Studies of the impact of monetary and fiscal policy on welfare have also to deal with the accuracy of the value function. Employing the base line stochastic growth model this paper compares the accuracy of the second order approximation and dynamic programming solution for both the policy as well as the value functions. We find that dynamic programming performs better with respect to both.

Suggested Citation

  • 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.
  • Handle: RePEc:sce:scecfa:469
    as

    Download full text from publisher

    File URL: http://www.math.uni-bayreuth.de/~lgruene/publ/secondorder.pdf
    File Function: main text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kim, Jinill & Kim, Sunghyun Henry, 2003. "Spurious welfare reversals in international business cycle models," Journal of International Economics, Elsevier, vol. 60(2), pages 471-500, August.
    2. 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.
    3. Manuel S. Santos & Jesus Vigo-Aguiar, 1998. "Analysis of a Numerical Dynamic Programming Algorithm Applied to Economic Models," Econometrica, Econometric Society, vol. 66(2), pages 409-426, March.
    4. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Optimal fiscal and monetary policy under sticky prices," Journal of Economic Theory, Elsevier, vol. 114(2), pages 198-230, February.
    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. 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. Wouter J. Den Haan & Albert Marcet, 1994. "Accuracy in Simulations," Review of Economic Studies, Oxford University Press, vol. 61(1), pages 3-17.
    8. 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.
    9. 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.
    10. 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.
    11. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
    12. Grune, Lars & Semmler, Willi, 2004. "Using dynamic programming with adaptive grid scheme for optimal control problems in economics," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2427-2456, December.
    13. Benigno, Pierpaolo & Woodford, Michael, 2006. "Optimal taxation in an RBC model: A linear-quadratic approach," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1445-1489.
    14. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    15. Gong, Gang & Semmler, Willi, 2006. "Stochastic Dynamic Macroeconomics: Theory and Empirical Evidence," OUP Catalogue, Oxford University Press, number 9780195301625.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Atolia, Manoj & Chatterjee, Santanu & Turnovsky, Stephen J., 2010. "How misleading is linearization? Evaluating the dynamics of the neoclassical growth model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1550-1571, September.
    2. Grüne, Lars & Semmler, Willi, 2008. "Asset pricing with loss aversion," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3253-3274, October.
    3. Mittnik, Stefan & Semmler, Willi, 2012. "Regime dependence of the fiscal multiplier," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 502-522.
    4. Juan Carlos Parra-Alvarez, 2013. "A comparison of numerical methods for the solution of continuous-time DSGE models," CREATES Research Papers 2013-39, Department of Economics and Business Economics, Aarhus University.
    5. Ernst, Ekkehard & Semmler, Willi, 2010. "Global dynamics in a model with search and matching in labor and capital markets," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1651-1679, September.
    6. Alfred Greiner & Willi Semmler & Tobias Mette, 2012. "An Economic Model of Oil Exploration and Extraction," Computational Economics, Springer;Society for Computational Economics, vol. 40(4), pages 387-399, December.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:sce:scecfa:469. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/sceeeea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.