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Diffusion index-based inflation forecasts for the euro area

In: Empirical studies of structural changes and inflation

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Author Info

  • Elena Angelini

    (European Central Bank)

  • Jérôme Henry

    (European Central Bank)

  • Ricardo Mestre

    (European Central Bank)

Abstract

Diffusion indexes based on dynamic factors have recently been advocated by Stock and Watson (1998), and further used to perform forecasting tests by the same authors on US data. This technique is explored for the euro area using a multi-country data set and a broad array of variables, in order to test the inflation forecasting performance of extracted factors at the aggregate euro area level. First, a description of factors extracted from different data sets is performed using a number of different approaches. Conclusions reached are that nominal phenomena in the original variables might be well captured in-sample using the factor approach. Out-of-sample tests have more ambiguous interpretation, as factors seem to be good leading indicators of inflation, but the comparative advantage of the factors is less clear. Nevertheless, alternative indicators such as unemployment or money growth do not outperform them JEL Classification: C53, E31, E37

(This abstract was borrowed from another version of this item.)

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This chapter was published in:

  • Bank for International Settlements, 2001. "Empirical studies of structural changes and inflation," BIS Papers, Bank for International Settlements, number 03, May.
    This item is provided by Bank for International Settlements in its series BIS Papers chapters with number 03-05.

    Handle: RePEc:bis:bisbpc:03-05

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    1. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
    2. Mario Forni & Lucrezia Reichlin, 1998. "Let's get real: a factor analytical approach to disaggregated business cycle dynamics," ULB Institutional Repository 2013/10147, ULB -- Universite Libre de Bruxelles.
    3. Danny Quah & Thomas J. Sargent, 1992. "A dynamic index model for large cross sections," Discussion Paper / Institute for Empirical Macroeconomics 77, Federal Reserve Bank of Minneapolis.
    4. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
    5. Angelini, Elena & Henry, Jérôme & Mestre, Ricardo, 2001. "A multi-country trend indicator for euro area inflation: computation and properties," Working Paper Series 0060, European Central Bank.
    6. Forni, Mario & Lippi, Marco, 2000. "The Generalized Dynamic Factor Model: Representation Theory," CEPR Discussion Papers 2509, C.E.P.R. Discussion Papers.
    7. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    8. 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.
    9. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, December.
    10. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
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