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Growth forecasts using time series and growth models

Author

Listed:
  • Kraay, Aart
  • Monokroussos, George

Abstract

The authors consider two alternative methods of forecasting real per capita GDP at various horizons: 1) univariate time series models estimated country by country; and 2) cross-country growth regressions. They evaluate the out-of-sample forecasting performance of both approaches for a large sample of industrial and developing countries. They find only modest differences between the two approaches. In almost all cases, differences in median (across countries) forecast performance are small relative to the large discrepancies between forecasts and actual outcomes. Interestingly, the performance of both models is similar to that of forecasts generated by the World Bank's Unified Survey. The results do not provide a compelling case for one approach over another, but they do indicate that there are potential gains from combining time series and growth-regression-based forecasting approaches.

Suggested Citation

  • Kraay, Aart & Monokroussos, George, 1999. "Growth forecasts using time series and growth models," Policy Research Working Paper Series 2224, The World Bank.
  • Handle: RePEc:wbk:wbrwps:2224
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    File URL: http://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2000/05/26/000094946_99120905324244/Rendered/PDF/multi_page.pdf
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    References listed on IDEAS

    as
    1. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    2. Diebold, Francis X., 1989. "Forecast combination and encompassing: Reconciling two divergent literatures," International Journal of Forecasting, Elsevier, vol. 5(4), pages 589-592.
    3. Diebold, Francis X & Kilian, Lutz, 2000. "Unit-Root Tests Are Useful for Selecting Forecasting Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 265-273, July.
    4. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
    5. Michael J. Artis, 1996. "How Accurate Are the Imf's Short-Term Forecasts? Another Examination of the World Economic Outlook," IMF Working Papers 96/89, International Monetary Fund.
    6. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
    7. Vogelsang, Timothy J., 1997. "Wald-Type Tests for Detecting Breaks in the Trend Function of a Dynamic Time Series," Econometric Theory, Cambridge University Press, vol. 13(06), pages 818-848, December.
    8. repec:cup:etheor:v:13:y:1997:i:6:p:818-49 is not listed on IDEAS
    9. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
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    Citations

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    Cited by:

    1. Bloom, David E. & Canning, David & Fink, Gunther & Finlay, Jocelyn E., 2007. "Does age structure forecast economic growth?," International Journal of Forecasting, Elsevier, vol. 23(4), pages 569-585.
    2. Ianchovichina, Elena & Kacker, Pooja, 2005. "Growth trends in the developing world : country forecasts and determinants," Policy Research Working Paper Series 3775, The World Bank.
    3. Ahlburg, Dennis & Lindh, Thomas, 2007. "Long-run income forecasting," International Journal of Forecasting, Elsevier, vol. 23(4), pages 533-538.
    4. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.

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