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Nonlinear and stable perturbation-based approximations

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  • Den Haan, Wouter J.
  • De Wind, Joris
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    Abstract

    Users of regular higher-order perturbation approximations can face two problems: policy functions with odd oscillations and simulated data that explode. We propose a perturbation-based approximation that (i) does not have odd shapes, (ii) generates stable time paths, and (iii) avoids the drawbacks that hamper the pruned perturbation approach of Kim et al. (2008). For models with nontrivial nonlinearities, we find that our alternative and the pruned perturbation approximations give a good qualitative insight in the nonlinear aspects of the true solution, but can differ from the true solution in some quantitative aspects, especially during severe peaks and troughs.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

    Volume (Year): 36 (2012)
    Issue (Month): 10 ()
    Pages: 1477-1497

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    Handle: RePEc:eee:dyncon:v:36:y:2012:i:10:p:1477-1497

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    Web page: http://www.elsevier.com/locate/jedc

    Related research

    Keywords: Accuracy; Nonlinear numerical solutions;

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    Cited by:
    1. Benjamin Born & Johannes Pfeifer, 2014. "Risk Matters: A Comment," CESifo Working Paper Series 4793, CESifo Group Munich.
    2. Lilia Maliar & Serguei Maliar & Sébastien Villemot, 2013. "Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions," Computational Economics, Society for Computational Economics, vol. 42(3), pages 307-325, October.
    3. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial Conditions and Density Forecasts for US Output and Inflation," Working Papers 715, Queen Mary, University of London, School of Economics and Finance.
    4. Hikaru Saijo, 2013. "The Uncertainty Multiplier and Business Cycles," UTokyo Price Project Working Paper Series 017, University of Tokyo, Graduate School of Economics.
    5. Lan, Hong & Meyer-Gohde, Alexander, 2013. "Solving DSGE models with a nonlinear moving average," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2643-2667.
    6. Sudipto Karmakar, 2013. "Macroprudential Regulation and Macroeconomic Activity," Working Papers w201317, Banco de Portugal, Economics and Research Department.
    7. Hans Dewachter & Raf Wouters, 2012. "Endogenous risk in a DSGE model with capital-constrained financial intermediaries," Working Paper Research 235, National Bank of Belgium.
    8. 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.
    9. Viktors Ajevskis, 2013. "Non-Local Solutions to Dynamic Equilibrium Models: the Approximate Stable Manifolds Approach," Working Papers 2013/03, Latvijas Banka.
    10. Nicolas Petrosky-Nadeau & Lu Zhang, 2013. "Solving the DMP Model Accurately," NBER Working Papers 19208, National Bureau of Economic Research, Inc.
    11. 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.

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