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Forecasting Inflation using Economic Indicators: the Case of France

  • Bruneau, C.
  • De Bandt, O.
  • Flageollet, A.
  • Michaux, E.

In order to provide short run forecasts of headline and core HICP inflation for France, we assess the forecasting performance of a large set of economic indicators, individually and jointly, as well as using dynamic factor models. We run out-of-sample forecasts implementing the Stock and Watson (1999) methodology. It turns out that, according to usual statistical criteria, the combination of several indicators -in particular those derived from surveys- provides better results than dynamic factor models, even after pre-selection of the variables included in the panel. However, factors included in VAR models exhibit more stable forecasting performance over time. Results for HICP excluding unprocessed food and energy are very encouraging. Moreover, we show that it is possible to use forecasts on this indicator to project overall inflation.

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Paper provided by Banque de France in its series Working papers with number 101.

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Length: 33 pages
Date of creation: 2003
Date of revision:
Handle: RePEc:bfr:banfra:101
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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. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  2. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
  3. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
  4. Artis, Michael J & Banerjee, Anindya & Marcellino, Massimiliano, 2002. "Factor Forecasts for the UK," CEPR Discussion Papers 3119, C.E.P.R. Discussion Papers.
  5. Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
  6. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September.
  7. 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.
  8. Riccardo Cristadoro & Mario Forni & Lucrezia Reichlin & Giovanni Veronese, 2005. "A core inflation indicator for the Euro area," ULB Institutional Repository 2013/10131, ULB -- Universite Libre de Bruxelles.
  9. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  10. P.J.G. Vlaar & A.H.J. den Reijer, 2003. "Forecasting inflation: An art as well as a science!," DNB Staff Reports (discontinued) 107, Netherlands Central Bank.
  11. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  12. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
  13. 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.
  14. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
  15. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
  16. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
  17. Angelini, Elena & Henry, Jérôme & Mestre, Ricardo, 2001. "Diffusion index-based inflation forecasts for the euro area," Working Paper Series 0061, European Central Bank.
  18. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
  19. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895.
  20. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  21. Cláudia Duarte & António Rua, 2005. "Forecasting Inflation Through a Bottom-Up Approach: The Portuguese Case," Working Papers w200502, Banco de Portugal, Economics and Research Department.
  22. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
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