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A Dynamic Factor Model for World Trade Growth

  • Stéphanie Guichard
  • Elena Rusticelli

This paper reviews the main monthly indicators that could help forecasting world trade and compares different type of forecasting models using these indicators. In particular it develops dynamic factor models (DFM) which have the advantage of handling larger datasets of information than bridge models and allowing for the inclusion of numerous monthly indicators on a national and world-wide level such as financial indicators, transportation and shipping indices, supply and orders variables and information technology indices. The comparison of the forecasting performance of the DFMs with more traditional bridge equation models as well as autoregressive benchmarking models shows that, the dynamic factor approach seems to perform better, especially when a large set of indicators is used, but also that the marginal gains in adding indicators seems to diminish after a certain stage. Un modèle à facteurs dynamiques pour prévoir la croissance du commerce mondial Ce document passe en revue les principaux indicateurs mensuels pouvant aider á prévoir le commerce mondial et compare différents types de modèles de prévision utilisant ces indicateurs. En particulier, il développe des modèles á facteurs dynamiques (DFM) qui ont l'avantage de permettre l’utilisation de plus de séries que les modèles d’étalonnage et donc d’inclure des indicateurs mensuels au niveau national et mondial tels que les indicateurs financiers, de transport et d’expédition, d’approvisionnement et de carnets de commandes ou encore et de technologie de l’information. La comparaison de la performance de prévision des DFM avec des modèles d’étalonnage plus traditionnels ou des modèles autoregressifs montre que l'approche en facteurs dynamiques semble plus performante, surtout quand un vaste ensemble d'indicateurs est utilisé ; les gains marginaux en ajoutant des indicateurs semblent toutefois diminuer après un certain stade.

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File URL: http://dx.doi.org/10.1787/5kg9zbvvwqq2-en
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Paper provided by OECD Publishing in its series OECD Economics Department Working Papers with number 874.

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Date of creation: 31 May 2011
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Handle: RePEc:oec:ecoaaa:874-en
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  1. A. C. Harvey & Siem Jan Koopman, 2000. "Computing Observation Weights for Signal Extraction and Filtering," Econometric Society World Congress 2000 Contributed Papers 0888, Econometric Society.
  2. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
  3. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
  4. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, 01.
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  7. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2007. "Opening the Black Box: Structural Factor Models with Large Cross-Sections," Center for Economic Research (RECent) 008, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
  8. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2008. "Short-Term Forecasting of GDP Using Large Monthly Datasets: A Pseudo Real-Time Forecast Evaluation Exercise," Bank of Lithuania Working Paper Series 1, Bank of Lithuania.
  9. Matthias Burgert & Stephane Dees, 2009. "Forecasting World Trade: Direct Versus “Bottom-Up” Approaches," Open Economies Review, Springer, vol. 20(3), pages 385-402, July.
  10. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
  11. Angelini, Elena & Bańbura, Marta & Rünstler, Gerhard, 2008. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," Working Paper Series 0953, European Central Bank.
  12. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
  13. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  14. Calista Cheung & Stéphanie Guichard, 2009. "Understanding the World Trade Collapse," OECD Economics Department Working Papers 729, OECD Publishing.
  15. Nigel Pain & Franck Sédillot, 2005. "Indicator models of real GDP growth in the major OECD economies," OECD Economic Studies, OECD Publishing, vol. 2005(1), pages 167-217.
  16. Maximo Camacho & Gabriel Perez-Quiros, 2009. "Ñ-STING: España Short Term INdicator of Growth," Banco de Espa�a Working Papers 0912, Banco de Espa�a.
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