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Forecasting Global Flows

Author

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  • Skriner, Edith

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

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.

Suggested Citation

  • Skriner, Edith, 2007. "Forecasting Global Flows," Economics Series 214, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:214
    as

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    File URL: https://irihs.ihs.ac.at/id/eprint/1784
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Edwards, Sebastian, 1998. "Openness, Productivity and Growth: What Do We Really Know?," Economic Journal, Royal Economic Society, vol. 108(447), pages 383-398, March.
    5. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, September.
    6. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    7. 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.
    8. 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.
    9. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    10. Mr. Ashoka Mody & Antu Panini Murshid, 2002. "Growing Up with Capital Flows," IMF Working Papers 2002/075, International Monetary Fund.
    11. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
    12. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
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    More about this item

    Keywords

    International economics; time series models; forecasts; forecast evaluation;
    All these keywords.

    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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