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Forecasting the World Economy in the Short-Term

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Author Info
Audrone Jakaitiene () (Institute of Mathematics and Informatics, Akademijos st. 4, LT-08663 Vilnius, Lithuania.)
Stéphane Dées () (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)

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Abstract

Forecasting the world economy is a difficult task given the complex inter relationships within and across countries. This paper proposes a number of approaches to forecast short-term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of forecast accuracy and, second, at checking whether methods forecasting directly aggregate variables (direct approaches) outperform methods based on the aggregation of country-speci.c forecasts (bottom-up approaches). Overall, all methods perform better than a simple benchmark for short horizons (up to three months ahead). Among the forecasting approaches used, factor models appear to perform the best. Moreover, direct approaches outperform bottom-up ones for real variables, but not for prices. Finally, when country-specific forecasts are adjusted to match direct forecasts at the aggregate levels (top-down approaches), the forecast accuracy is neither improved nor deteriorated (i.e. top-down and bottom-up approaches are broadly equivalent in terms of country-specific forecast accuracy). JEL Classification: C53, C32, E37, F17.

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Paper provided by European Central Bank in its series Working Paper Series with number 1059.

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Length: 44 pages
Date of creation: Jun 2009
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Handle: RePEc:ecb:ecbwps:20091059

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Related research
Keywords: Factor models; Forecasts; Time series models.;

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  1. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May. [Downloadable!] (restricted)
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  2. McKibbin, W.J., 1999. "Forecasting the World Economy Using Dynamic Intertemporal General Equilibrium Multi-Country Models," Papers 145, Brookings Institution - Working Papers.
  3. M. Ayhan Kose & Christopher Otrok & Charles H. Whiteman, 2003. "International Business Cycles: World, Region, and Country-Specific Factors," American Economic Review, American Economic Association, vol. 93(4), pages 1216-1239, September. [Downloadable!]
  4. Antonello D’Agostino & Domenico Giannone & Paolo Surico, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series 605, European Central Bank. [Downloadable!]
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  5. Barhoumi, K. & Rünstler, G. & Cristadoro, R. & Den Reijer, A. & Jakaitiene, A. & Jelonek, P. & Rua, A. & Ruth, K. & Benk, S. & Van Nieuwenhuyze, C., 2008. "Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise," Documents de Travail 215, Banque de France. [Downloadable!]
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  6. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, 02. [Downloadable!] (restricted)
  7. Wim Suyker & Gerard van Welzenis, 2005. "Explanatory note on the CPB world trade series," CPB Memoranda 116, CPB Netherlands Bureau for Economic Policy Analysis. [Downloadable!]
  8. Moriguchi, C, 1973. "Forecasting and Simulation Analysis of the World Economy," American Economic Review, American Economic Association, vol. 63(2), pages 402-09, May.
  9. Marta Banbura & Gerhard Rünstler, 2007. "A look into the factor model black box - publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 751, European Central Bank. [Downloadable!]
  10. Pesaran, M.H. & Schuermann, T. & Smit, L.V., 2008. "Forecasting Economic and Financial Variables with Global VARs," Cambridge Working Papers in Economics 0807, Faculty of Economics, University of Cambridge. [Downloadable!]
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  11. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
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