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Forecasting Global Flows

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  • Edith Skriner
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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 comovement 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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    Bibliographic Info

    Paper provided by FIW in its series FIW Working Paper series with number 009.

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    Length: 50
    Date of creation: Jan 2008
    Date of revision:
    Handle: RePEc:wsr:wpaper:y:2008:i:009

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    Postal: FIW Project Office Austrian Institute of Economic Research Arsenal Objekt 20 A-1030 Vienna
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    Related research

    Keywords: International Economics; Time Series Models; Forecasts; Forecast Evaluation; Global Flows;

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    References

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    1. Ashoka Mody & Antu Panini Murshid, 2002. "Growing Up with Capital Flows," IMF Working Papers 02/75, International Monetary Fund.
    2. 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.
    3. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
    4. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06.
    5. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.
    6. James D. Hamilton, 2000. "What is an Oil Shock?," NBER Working Papers 7755, National Bureau of Economic Research, Inc.
    7. Inoue, Atsushi & Kilian, Lutz, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers.
    8. Christopher A. Sims, 1992. "Interpreting the Macroeconomic Time Series Facts: The Effects of Monetary Policy," Cowles Foundation Discussion Papers 1011, Cowles Foundation for Research in Economics, Yale University.
    9. 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.
    10. Cashin, Paul & McDermott, C. John & Scott, Alasdair, 2002. "Booms and slumps in world commodity prices," Journal of Development Economics, Elsevier, vol. 69(1), pages 277-296, October.
    11. Eswar S. Prasad & Jeffery A. Gable, 1998. "International Evidence on the Determinants of Trade Dynamics," IMF Staff Papers, Palgrave Macmillan, vol. 45(3), pages 401-439, September.
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