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A Theory Of Pruning

Author

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  • Giovanni Lombardo
  • Harald Uhlig

Abstract

Often, numerical simulations for dynamic, stochastic models in economics are needed. Kim et al. (Journal of Economic Dynamics and Control 32 2008, 3397–414) proposed “pruning” to deal with the challenge of generating explosive paths when employing second‐order approximations. In this article, we provide a theory of pruning and formulas for pruning of any order. Our approach builds on Judd's Numerical Methods in Economics (1998), chapter 13. We provide a comparison to existing methods.

Suggested Citation

  • Giovanni Lombardo & Harald Uhlig, 2018. "A Theory Of Pruning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(4), pages 1825-1836, November.
  • Handle: RePEc:wly:iecrev:v:59:y:2018:i:4:p:1825-1836
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    File URL: https://doi.org/10.1111/iere.12321
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    References listed on IDEAS

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    1. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, March.
    2. Lombardo, Giovanni, 2010. "On approximating DSGE models by series expansions," Working Paper Series 1264, European Central Bank.
    3. Den Haan, Wouter J. & De Wind, Joris, 2012. "Nonlinear and stable perturbation-based approximations," Journal of Economic Dynamics and Control, Elsevier, vol. 36(10), pages 1477-1497.
    4. 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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    Cited by:

    1. Ag�nor, Pierre-Richard & Gambacorta, Leonardo & Kharroubi, Enisse & Lombardo, Giovanni & Pereira da Silva, Luiz A., 2017. "The International Dimensions of Macroprudential Policies," CEPR Discussion Papers 12108, C.E.P.R. Discussion Papers.
    2. Mutschler, Willi, 2018. "Higher-order statistics for DSGE models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 44-56.
    3. Mutschler, Willi, 2015. "Identification of DSGE models—The effect of higher-order approximation and pruning," Journal of Economic Dynamics and Control, Elsevier, vol. 56(C), pages 34-54.
    4. Mutschler, Willi, 2015. "Note on Higher-Order Statistics for the Pruned-State-Space of nonlinear DSGE models," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113138, Verein für Socialpolitik / German Economic Association.
    5. repec:eee:macchp:v2-1641 is not listed on IDEAS
    6. Borovicka, J. & Hansen, L.P., 2016. "Term Structure of Uncertainty in the Macroeconomy," Handbook of Macroeconomics, Elsevier.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium

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