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Are disaggregate data useful for factor analysis in forecasting French GDP?

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Author Info

  • Karim Barhoumi

    (Banque de France, DGEI-DCPM, Paris, France)

  • Olivier Darné
  • Laurent Ferrara

    (Banque de France, DGEI-DCPM, Paris, France)

Abstract

This paper compares the GDP forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components obtained using time and frequency domain methods. We question whether it is more appropriate to use aggregate or disaggregate data to extract the factors used in forecasting equations. The forecasting accuracy is evaluated for various forecast horizons considering both rolling and recursive schemes. We empirically show that static factors, estimated from a small database, lead to competitive results, especially for nowcasting. Copyright © 2009 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1162
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 29 (2010)
Issue (Month): 1-2 ()
Pages: 132-144

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Handle: RePEc:jof:jforec:v:29:y:2010:i:1-2:p:132-144

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Anna Norin, 2011. "Nowcasting of the Gross Regional Product," ERSA conference papers ersa10p768, European Regional Science Association.
  2. Catherine Doz & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Post-Print hal-00844811, HAL.
  3. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
  4. Stéphanie Guichard & Elena Rusticelli, 2011. "A Dynamic Factor Model for World Trade Growth," OECD Economics Department Working Papers 874, OECD Publishing.
  5. Marie Bessec, 2013. "Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 500-511, 09.
  6. Banbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
  7. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
  8. Branimir Jovanovic & Magdalena Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," Working Papers 2010-02, National Bank of the Republic of Macedonia, revised Aug 2010.
  9. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
  10. Bellégo, C. & Ferrara, L., 2009. "Forecasting Euro-area recessions using time-varying binary response models for financial," Working papers 259, Banque de France.
  11. Branimir, Jovanovic & Magdalena, Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," MPRA Paper 43162, University Library of Munich, Germany.
  12. Antipa, P. & Barhoumi, K. & Brunhes-Lesage, V. & Darné, O., 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Working papers 401, Banque de France.
  13. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.

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