IDEAS home Printed from https://ideas.repec.org/p/tin/wpaper/20100040.html
   My bibliography  Save this paper

Is Economic Recovery a Myth? Robust Estimation of Impulse Responses

Author

Listed:
  • Coen N. Teulings

    (CPB, The Hague, and University of Amsterdam)

  • Nick Zubanov

    (CPB, The Hague)

Abstract

See the publication in the 'Journal of Applied Econometrics' (2014). We estimate the impulse response function (IRF) of GDP toa banking crisis, applying an extension of the local projectionsmethod developed in Jorda (2005). This method is shown to bemore robust to misspecification than calculating IRFs analytically. However, it suffers from a hitherto unnoticed systematicbias which increases with the forecast horizon. We propose asimple correction to this bias, which our Monte Carlo simulations show works well. Applying our corrected local projectionsestimator to a panel of 99 countries observed between 1974-2001,we find that an average banking crisis yields a long-term GDP lossof around 10 percent with little sign of recovery within 10 years.GDP losses to banking crises are even more severe in Africancountries. Like the original Jorda's (2005) method, our extensionof it is quite widely applicable.

Suggested Citation

  • Coen N. Teulings & Nick Zubanov, 2010. "Is Economic Recovery a Myth? Robust Estimation of Impulse Responses," Tinbergen Institute Discussion Papers 10-040/3, Tinbergen Institute, revised 07 Jul 2011.
  • Handle: RePEc:tin:wpaper:20100040
    as

    Download full text from publisher

    File URL: http://papers.tinbergen.nl/10040.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Valerie Cerra & Sweta Chaman Saxena, 2008. "Growth Dynamics: The Myth of Economic Recovery," American Economic Review, American Economic Association, vol. 98(1), pages 439-457, March.
    2. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    3. John Y. Campbell & N. Gregory Mankiw, 1987. "Are Output Fluctuations Transitory?," The Quarterly Journal of Economics, Oxford University Press, vol. 102(4), pages 857-880.
    4. Cai, Xiaoming & Den Haan, Wouter, 2009. "Predicting recoveries and the importance of using enough information," CEPR Discussion Papers 7508, C.E.P.R. Discussion Papers.
    5. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    6. Yanping Chong & Òscar Jordà & Alan M. Taylor, 2012. "The Harrod–Balassa–Samuelson Hypothesis: Real Exchange Rates And Their Long‐Run Equilibrium," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(2), pages 609-634, May.
    7. Jon Faust & Jonathan H. Wright, 2008. "Efficient Prediction of Excess Returns," NBER Working Papers 14169, National Bureau of Economic Research, Inc.
    8. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    9. Jon Faust & Jonathan H. Wright, 2011. "Efficient Prediction of Excess Returns," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 647-659, May.
    10. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    11. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    banking crisis; impulse response; panel data;

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:tin:wpaper:20100040. 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: (Tinbergen Office +31 (0)10-4088900). General contact details of provider: http://edirc.repec.org/data/tinbenl.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.