IDEAS home Printed from https://ideas.repec.org/p/zbw/bubdp1/5573.html
   My bibliography  Save this paper

Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities

Author

Listed:
  • Scharnagl, Michael
  • Schumacher, Christian

Abstract

This paper addresses the relative importance of monetary indicators for forecasting inflation in the euro area in a Bayesian framework. Bayesian Model Averaging (BMA)based on predictive likelihoods provides a framework that allows for the estimation of inclusion probabilities of a particular variable, that is the probability of that variable being in the forecast model. A novel aspect of the paper is the discussion of group-wise inclusion probabilities, which helps to address the empirical question whether the group of monetary variables is relevant for forecasting euro area inflation. In our application, we consider about thirty monetary and non-monetary indicators for inflation. Using this data, BMA provides inclusion probabilities and weights for Bayesian forecast combination. The empirical results for euro area data show that monetary aggregates and non-monetary indicators together play an important role for forecasting inflation, whereas the isolated information content of both groups is limited. Forecast combination can only partly outperform single-indicator benchmark models.

Suggested Citation

  • Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp1:5573
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/19686/1/200709dkp.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Gerlach, Stefan & Svensson, Lars E. O., 2003. "Money and inflation in the euro area: A case for monetary indicators?," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1649-1672, November.
    2. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    3. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    4. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier.
    5. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    6. Antonello D'Agostino & Domenico Giannone & Paolo Surico, 2005. "(Un)Predictability and Macroeconomic Stability," Macroeconomics 0510024, EconWPA.
    7. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?," Discussion Paper Series 1: Economic Studies 2006,32, Deutsche Bundesbank.
    8. Jana Eklund & Sune Karlsson, 2007. "Forecast Combination and Model Averaging Using Predictive Measures," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 329-363.
    9. Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
    10. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    11. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    12. Paul De Grauwe & Magdalena Polan, 2005. "Is Inflation Always and Everywhere a Monetary Phenomenon?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 107(2), pages 239-259, June.
    13. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January.
    14. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro-area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    15. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
    16. Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
    17. Assenmacher-Wesche, Katrin & Gerlach, Stefan, 2006. "Understanding the Link between Money Growth and Inflation in the Euro Area," CEPR Discussion Papers 5683, C.E.P.R. Discussion Papers.
    18. Nicoletti-Altimari, Sergio, 2001. "Does money lead inflation in the euro area?," Working Paper Series 0063, European Central Bank.
    19. 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.
    20. Ley, Eduardo & Steel, Mark F.J., 2007. "Jointness in Bayesian variable selection with applications to growth regression," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 476-493, September.
    21. Stefan Gerlach, 2004. "The two pillars of the European Central Bank," Economic Policy, CEPR;CES;MSH, vol. 19(40), pages 389-439, October.
    22. Joaquim Vieira Ferreira Levy & Alessandro Calza & Dieter Gerdesmeier, 2001. "Euro Area Money Demand; Measuring the Opportunity Costs Appropriately," IMF Working Papers 01/179, International Monetary Fund.
    23. Neumann, Manfred J. M. & Greiber, Claus, 2004. "Inflation and core money growth in the euro area," Discussion Paper Series 1: Economic Studies 2004,36, Deutsche Bundesbank.
    24. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Helge Berger & Pär Österholm, 2011. "Does Money matter for U.S. Inflation? Evidence from Bayesian VARs," CESifo Economic Studies, CESifo, vol. 57(3), pages 531-550, September.
    2. Helge Berger & Pär Österholm, 2011. "Does Money Growth Granger Cause Inflation in the Euro Area? Evidence from Out‐of‐Sample Forecasts Using Bayesian VARs," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 45-60, March.
    3. Emil Stavrev & Helge Berger, 2012. "The information content of money in forecasting euro area inflation," Applied Economics, Taylor & Francis Journals, vol. 44(31), pages 4055-4072, November.
    4. Berger, Helge & Österholm, Pär, 2008. "Does money growth granger-cause inflation in the Euro Area? Evidence from output-of-sample forecasts using Bayesian VARs," Discussion Papers 2008/10, Free University Berlin, School of Business & Economics.
    5. Belke, Ansgar & Orth, Walter & Setzer, Ralph, 2008. "Liquidity and the dynamic pattern of price adjustment: a global view," Discussion Paper Series 1: Economic Studies 2008,25, Deutsche Bundesbank.
    6. Berger, Helge & Harjes, Thomas & Stavrev, Emil, 2008. "The ECB's monetary analysis revisited," Discussion Papers 2008/14, Free University Berlin, School of Business & Economics.

    More about this item

    Keywords

    inflation forecasting; monetary indicators; Bayesian Model Averaging; inclusion probability;

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

    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:bubdp1:5573. 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: (ZBW - German National Library of Economics). General contact details of provider: http://edirc.repec.org/data/dbbgvde.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.