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The financial content of inflation risks in the euro area

  • Andrade, P.
  • Fourel, V.
  • Ghysels, E.
  • Idier, I.

Recent studies emphasize that survey-based inflation risk measures are informative about future inflation and thus useful for monetary authorities. However, these data are typically available at a quarterly frequency whereas monetary policy decisions require a more frequent monitoring of such risks. Using the ECB survey of professional forecasters, we show that high-frequency financial market data have predictive power for the low-frequency survey-based inflation risk indicators observed at the end of a quarter. We rely on MIDAS regressions to handle the problem of mixing data with different frequencies that such an analysis implies. We also illustrate that upside and downside risks react differently to financial indicators.

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Paper provided by Banque de France in its series Working papers with number 437.

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Length: 35 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:bfr:banfra:437
Contact details of provider: Postal: Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS
Web page: http://www.banque-france.fr/

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  1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2003. "Leading Indicators for Euro-area Inflation and GDP Growth," Working Papers 235, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  2. Libero Monteforte & Gianluca Moretti, . "Real time forecasts of inflation: the role of financial variables," Working Papers wp2011-6, Department of the Treasury, Ministry of the Economy and of Finance.
  3. Jens H.E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2012. "Extracting Deflation Probability Forecasts from Treasury Yields," International Journal of Central Banking, International Journal of Central Banking, vol. 8(4), pages 21-60, December.
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  7. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier.
  8. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
  9. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  10. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
  11. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2007. "The ECB survey of professional forecasters (SPF) – A review after eight years’ experience," Occasional Paper Series 59, European Central Bank.
  12. Andrade, P. & Ghysels, E. & Idier, J., 2012. "Tails of Inflation Forecasts and Tales of Monetary Policy," Working papers 407, Banque de France.
  13. Kilian, Lutz & Manganelli, Simone, 2007. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," CEPR Discussion Papers 6031, C.E.P.R. Discussion Papers.
  14. Andrade, Philippe & Le Bihan, Hervé, 2013. "Inattentive professional forecasters," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 967-982.
  15. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116 - 159.
  16. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
  17. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, 05.
  18. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  19. Michael F. Bryan & Stephen G. Cecchetti & Rodney L. Wiggins II, 1997. "Efficient Inflation Estimation," NBER Working Papers 6183, National Bureau of Economic Research, Inc.
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  21. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  22. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
  23. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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