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A theory of pruning

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  • Lombardo, Giovanni
  • Uhlig, Harald

Abstract

Often, numerical simulations for dynamic, stochastic models in economics are needed. Higher order methods can be attractive, but bear the danger of generating explosive solutions in originally stationary models. Kim-Kim-Schaumburg-Sims (2008) proposed pruning to deal with this challenge for second order approximations. In this paper, we provide a theory of pruning and formulas for pruning of any order. We relate it to results described by Judd (1998) on perturbing dynamical systems. JEL Classification: C63, C02, C62

Suggested Citation

  • Lombardo, Giovanni & Uhlig, Harald, 2014. "A theory of pruning," Working Paper Series 1696, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20141696
    Note: 656519
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp1696.pdf
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    Other versions of this item:

    • 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.

    References listed on IDEAS

    as
    1. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
    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.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Gambacorta, Leonardo & Agénor, Pierre-Richard & 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, 2015. "Note on Higher-Order Statistics for the Pruned-State-Space of nonlinear DSGE models," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113138, Verein für Socialpolitik / German Economic Association.
    3. Thierry Betti & Thomas Coudert, 2022. "How harmful are cuts in public employment and wage in times of high unemployment?," Bulletin of Economic Research, Wiley Blackwell, vol. 74(1), pages 247-277, January.
    4. Thierry BETTI & Thomas COUDERT, 2015. "How can the labor market accounts for the effectiveness of fiscal policy over the business cycle?," Working Papers of LaRGE Research Center 2015-06, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
    5. Levieuge, Grégory & Sahuc, Jean-Guillaume, 2021. "Downward interest rate rigidity," European Economic Review, Elsevier, vol. 137(C).
    6. Borovicka, J. & Hansen, L.P., 2016. "Term Structure of Uncertainty in the Macroeconomy," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1641-1696, Elsevier.
    7. Martin M. Andreasen & Anders Kronborg, 2017. "The Extended Perturbation Method: New Insights on the New Keynesian Model," CREATES Research Papers 2017-14, Department of Economics and Business Economics, Aarhus University.
    8. 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.
    9. Mutschler, Willi, 2018. "Higher-order statistics for DSGE models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 44-56.
    10. Ajevskis Viktors, 2017. "Semi-global solutions to DSGE models: perturbation around a deterministic path," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-28, April.
    11. Viktors Ajevskis, 2019. "Generalised Impulse Response Function as a Perturbation of a Global Solution to DSGE Models," Working Papers 2019/04, Latvijas Banka.
    12. Yu-Ting Chiang, 2022. "Attention and Fluctuations in Macroeconomic Uncertainty," Working Papers 2022-004, Federal Reserve Bank of St. Louis, revised 09 Nov 2023.

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    More about this item

    Keywords

    numerical economics; numerical simulation; Perturbation Methods; pruning; Taylor expansion;
    All these keywords.

    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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