[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/p/ube/dpvwib/dp2306.html
   My bibliography  Save this paper

Impulse Response Analysis at the Zero Lower Bound

Author

Listed:
  • Luca Benati
  • Thomas A. Lubik
Abstract
We study whether the response of the economy to structural shocks changes at the zero lower bound. Monte Carlo evidence suggests that VARs have a limited ability to detect changes in impulse response functions at the ZLB compared to the standard environment with positive interest rates. This issue is confounded given the short sample lengths that characterize ZLB episodes. This is especially the case for timevarying parameter VARs, whose estimates are two-sided, and therefore tend to smooth changes across regimes. In contrast, fixed-coefficient VARs estimated by sub-sample exhibit greater power. Pooled estimates from panel VARs for six countries based on (long-run and) sign restrictions detect in several instances changes in the IRFs. This evidence is, however, weaker than it appears. Based on (long-run and) sign restrictions we find that prior and posterior IRFs are often close, so that the concern raised by Baumeister and Hamilton (2015) appears to be relevant. Evidence from a multivariate permanent-transitory decomposition of GDP shocks is markedly sharper. It points towards material changes in the IRFs: at the ZLB the IRFs of GDP and unemployment exhibit more inertia, the response of prices is flatter, and the responses of interest rates are weaker.

Suggested Citation

  • Luca Benati & Thomas A. Lubik, 2023. "Impulse Response Analysis at the Zero Lower Bound," Diskussionsschriften dp2306, Universitaet Bern, Departement Volkswirtschaft.
    • Handle: RePEc:ube:dpvwib:dp2306
    as

    Download full text from publisher

    File URL: https://repec.vwiit.ch/dp/dp2306.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benhabib, Jess & Schmitt-Grohe, Stephanie & Uribe, Martin, 2001. "The Perils of Taylor Rules," Journal of Economic Theory, Elsevier, vol. 96(1-2), pages 40-69, January.
    2. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    3. Fernández-Villaverde, Jesús & Gordon, Grey & Guerrón-Quintana, Pablo & Rubio-Ramírez, Juan F., 2015. "Nonlinear adventures at the zero lower bound," Journal of Economic Dynamics and Control, Elsevier, vol. 57(C), pages 182-204.
    4. Davide Debortoli & Jordi Galí & Luca Gambetti, 2020. "On the Empirical (Ir)Relevance of the Zero Lower Bound Constraint," NBER Macroeconomics Annual, University of Chicago Press, vol. 34(1), pages 141-170.
    5. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    6. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2015. "Inflation in the Great Recession and New Keynesian Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 168-196, January.
    7. Jess Benhabib & Stephanie Schmitt-Grohe & Martin Uribe, 2002. "Avoiding Liquidity Traps," Journal of Political Economy, University of Chicago Press, vol. 110(3), pages 535-563, June.
    8. Lawrence Christiano & Martin Eichenbaum & Sergio Rebelo, 2011. "When Is the Government Spending Multiplier Large?," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 78-121.
    9. Luca Benati, 2008. "Investigating Inflation Persistence Across Monetary Regimes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(3), pages 1005-1060.
    10. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    11. Boneva, Lena Mareen & Braun, R. Anton & Waki, Yuichiro, 2016. "Some unpleasant properties of loglinearized solutions when the nominal rate is zero," Journal of Monetary Economics, Elsevier, vol. 84(C), pages 216-232.
    12. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    13. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    14. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-353, July.
    15. Benati, Luca, 2015. "The long-run Phillips curve: A structural VAR investigation," Journal of Monetary Economics, Elsevier, vol. 76(C), pages 15-28.
    16. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 345-361.
    17. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    18. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July.
    19. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    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. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Paper series 20-09, Rimini Centre for Economic Analysis.
    2. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    3. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    4. Canova, Fabio & Ferroni, Filippo, 2020. "A hitchhiker guide to empirical macro models," CEPR Discussion Papers 15446, C.E.P.R. Discussion Papers.
    5. Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, 2020. "Uniform Priors for Impulse Responses," Working Papers 22-30, Federal Reserve Bank of Philadelphia.
    6. Benati, Luca, 2015. "The long-run Phillips curve: A structural VAR investigation," Journal of Monetary Economics, Elsevier, vol. 76(C), pages 15-28.
    7. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    8. Klug, Thorsten & Mayer, Eric & Schuler, Tobias, 2022. "The corporate saving glut and the current account in Germany," Journal of International Money and Finance, Elsevier, vol. 121(C).
    9. Francesco Bianchi & Leonardo Melosi, 2017. "Escaping the Great Recession," American Economic Review, American Economic Association, vol. 107(4), pages 1030-1058, April.
    10. Mark Bognanni, 2018. "A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification," Working Papers (Old Series) 1811, Federal Reserve Bank of Cleveland.
    11. Francesco Furlanetto & Ørjan Robstad & Pål Ulvedal & Antoine Lepetit, 2020. "Estimating hysteresis effects," Working Paper 2020/13, Norges Bank.
    12. Luca Benati & Thomas A. Lubik, 2021. "Searching for Hysteresis," Working Paper 21-03, Federal Reserve Bank of Richmond.
    13. Taisuke Nakata, 2018. "Reputation and Liquidity Traps," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 28, pages 252-268, April.
    14. Drago Bergholt & Francesco Furlanetto & Etienne Vaccaro-Grange, 2023. "Did monetary policy kill the Phillips Curve? Some simple arithmetics," Working Paper 2023/2, Norges Bank.
    15. Andrew Binning & Junior Maih, 2016. "Implementing the Zero Lower Bound in an Estimated Regime-Switching DSGE Model," Working Papers No 3/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    16. Stéphane Lhuissier & Benoît Mojon & Juan Rubio-Ramírez, 2020. "Does the Liquidity Trap Exist?," Working Papers 2020-04, FEDEA.
    17. Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
    18. Fisher, Lance A. & Huh, Hyeon-seung, 2019. "An IV framework for combining sign and long-run parametric restrictions in SVARs," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    19. Baumeister, Christiane & Hamilton, James D., 2021. "Reprint: Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 114(C).
    20. Cars Hommes & Domenico Massaro & Isabelle Salle, 2019. "Monetary And Fiscal Policy Design At The Zero Lower Bound: Evidence From The Lab," Economic Inquiry, Western Economic Association International, vol. 57(2), pages 1120-1140, April.

    More about this item

    Keywords

    Zero Lower Bound; Bayesian VARs; structural VARs; monetary policy; sign restrictions;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:ube:dpvwib:dp2306. 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: Franz Koelliker (email available below). General contact details of provider: https://edirc.repec.org/data/vwibech.html .

    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.