IDEAS home Printed from https://ideas.repec.org/p/zbw/bubdp1/6343.html
   My bibliography  Save this paper

Spill-over effects of monetary policy: a progress report on interest rate convergence in Europe

Author

Listed:
  • Fladung, Michael

Abstract

This study examines differences in the interest rate response to an ECB policy impulse in the euro area, the new EU-member states, and in the other non-eurozone EU countries in order to gauge the degree of interest rate alignment in Europe. To this end, PANIC, a Panel Analysis of Non-stationarity in I diosyncratic and Common components, is employed in a structural factor set-up. Under the assumption that the ECB sets the short end of the yield curve, the analysis shows that : (i) The response of Europe's money and government bond markets to new information can be summarized by two common stochastic trends and one stationary common factor, which together explain more than 68% of the overall variation of the two market segments; (ii) one of the factor innovations can be associated with the ECB's policy stance, which strongly affects the short end of the euro area's yield curve; (iii) compared to the euro area, the short-term market segments in the new EU-member states react, on average, 12% more weakly to the monetary policy signal, whereas these countries' long-term government bond yields respond up to 25% more strongly to such a common innovation.

Suggested Citation

  • Fladung, Michael, 2007. "Spill-over effects of monetary policy: a progress report on interest rate convergence in Europe," Discussion Paper Series 1: Economic Studies 2007,27, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp1:6343
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/19704/1/200727dkp.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Reichlin, Lucrezia & Sala, Luca & Giannone, Domenico, 2002. "Tracking Greenspan: Systematic and Unsystematic Monetary Policy Revisited," CEPR Discussion Papers 3550, C.E.P.R. Discussion Papers.
    4. Carstensen, Kai, 2003. "Nonstationary term premia and cointegration of the term structure," Economics Letters, Elsevier, vol. 80(3), pages 409-413, September.
    5. Marco Lippi & Daniel L. Thornton, 2004. "A dynamic factor analysis of the response of U. S. interest rates to news," Working Papers 2004-013, Federal Reserve Bank of St. Louis.
    6. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    7. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224, National Bureau of Economic Research, Inc.
    8. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    9. Monika Piazzesi, 2005. "Bond Yields and the Federal Reserve," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 311-344, April.
    10. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    11. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    12. Ignazio Angeloni & Michael Flad & Francesco Paolo Mongelli, 2005. "Economic and monetary integration of the new Member States - helping to chart the route," Occasional Paper Series 36, European Central Bank.
    13. Kim, Suk-Joong & Lucey, Brian M. & Wu, Eliza, 2006. "Dynamics of bond market integration between established and accession European Union countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(1), pages 41-56, February.
    14. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    15. Fendel, Ralf, 2004. "Towards a Joint Characterization of Monetary Policy and the Dynamics of the Term Structure of Interest Rates," Discussion Paper Series 1: Economic Studies 2004,24, Deutsche Bundesbank.
    16. Hall, Anthony D & Anderson, Heather M & Granger, Clive W J, 1992. "A Cointegration Analysis of Treasury Bill Yields," The Review of Economics and Statistics, MIT Press, vol. 74(1), pages 116-126, February.
    17. Solnik, Bruno, 1983. "International Arbitrage Pricing Theory," Journal of Finance, American Finance Association, vol. 38(2), pages 449-457, May.
    18. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    19. Angeloni, Ignazio & Flad, Michael & Mongelli, Francesco Paolo, 2005. "Economic and monetary integration of New Member States - helping to chart the route," Occasional Paper Series 36, European Central Bank.
    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. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    2. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    3. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    4. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    5. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    6. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    7. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    8. Houssa, Romain, 2008. "Monetary union in West Africa and asymmetric shocks: A dynamic structural factor model approach," Journal of Development Economics, Elsevier, vol. 85(1-2), pages 319-347, February.
    9. Jörg Breitung & In Choi, 2013. "Factor models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 11, pages 249-265, Edward Elgar Publishing.
      • In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2011.
    10. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
    11. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.
    12. Erdemlioglu, Deniz, 2009. "Macro Factors in UK Excess Bond Returns: Principal Components and Factor-Model Approach," MPRA Paper 28895, University Library of Munich, Germany.
    13. Philipp Gersing & Christoph Rust & Manfred Deistler, 2023. "Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Jan 2024.
    14. Reichlin, Lucrezia, 2002. "Factor Models in Large Cross-Sections of Time Series," CEPR Discussion Papers 3285, C.E.P.R. Discussion Papers.
    15. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    16. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    17. Kemal Bagzibagli, 2014. "Monetary transmission mechanism and time variation in the Euro area," Empirical Economics, Springer, vol. 47(3), pages 781-823, November.
    18. António Rua & Francisco Craveiro Dias, 2008. "Determining the number of factors in approximate factor models with global and group-specific factors," Working Papers w200809, Banco de Portugal, Economics and Research Department.
    19. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
    20. Lippi, Marco & Reichlin, Lucrezia & Forni, Mario, 2003. "Opening the Black Box: Structural Factor Models versus Structural VARs," CEPR Discussion Papers 4133, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    Factor Models; Common Stochastic Trends; Interest Rate Channel; New Member States; Mixed Data Sampling;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

    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:zbw:bubdp1:6343. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/dbbgvde.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.