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Forecasting Global Flows

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  • Skriner, Edith

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

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    Abstract

    The theory suggests that investment activities and monetary policy influence the development of the global business cycle. The oil price and other raw material prices also play a key role in the economic development and there is a co-movement among oil consumption and global output. Therefore, the aim of this study is to explain the development of this set of variables by ARs, small-scale VARs and ECMs. The lag length and the rank of the time series models have been determined using information criteria. Then one-step ahead forecasts have been generated. It was found, that the ARs generate the best forecasts at the beginning of the forecasting horizon. However, when the forecasting horizon increases the VARs outperform the ARs. Comparing the forecasting performance of the ECMs, it was found that the forecasting ability of the ECMs in first differences outperform the level based ECMs when the forecasting horizon increases.

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    File URL: http://www.ihs.ac.at/publications/eco/es-214.pdf
    File Function: First version, 2007
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    Bibliographic Info

    Paper provided by Institute for Advanced Studies in its series Economics Series with number 214.

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    Length: 38 pages
    Date of creation: Jul 2007
    Date of revision:
    Handle: RePEc:ihs:ihsesp:214

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    Postal: Institute for Advanced Studies - Library, Stumpergasse 56, A-1060 Vienna, Austria

    Related research

    Keywords: International economics; time series models; forecasts; forecast evaluation;

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    1. C. John McDermott & Paul Cashin & Alasdair Scott, 1999. "Booms and Slumps in World Commodity Prices," IMF Working Papers 99/155, International Monetary Fund.
    2. David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
    3. Mody, Ashoka & Murshid, Antu Panini, 2005. "Growing up with capital flows," Journal of International Economics, Elsevier, vol. 65(1), pages 249-266, January.
    4. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, October.
    5. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
    6. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    7. Inoue, Atsushi & Kilian, Lutz, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers.
    8. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers.
    9. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    10. Eswar Prasad & Jeffery A. Gable, 1997. "International Evidenceon the Determinants of Trade Dynamics," IMF Working Papers 97/172, International Monetary Fund.
    11. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
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