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Combination of Forecasts in the International Context: Predicting Debt Reschedulings

Author

Listed:
  • Briance Mascarenhas

    (New York University)

  • Ole Christian Sand

    (Elkon Finans A/S (Oslo, Norway))

Abstract

International business is characterized by high uncertainty, making accurate forecasting both important and difficult. Development in forecasting methodology are examined in the prediction of international debt reschedulings. Three basic forecasting approaches are evaluated: single method forecasts, raw data combination forecasts, and forecast-combinations. All three approaches exhibited significant improvement in prediction accuracy over a naive forecasting approach, but only the odds-matrix forecast-combination approach outperformed another non-native approach. The odds-matrix forecast-combination method is less sensitive to data sparsity and unreliability and has the potential to synthesize, effectively and in a theoretically based way, diverse perspectives in the international context.© 1989 JIBS. Journal of International Business Studies (1989) 20, 539–552

Suggested Citation

  • Briance Mascarenhas & Ole Christian Sand, 1989. "Combination of Forecasts in the International Context: Predicting Debt Reschedulings," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 20(3), pages 539-552, September.
  • Handle: RePEc:pal:jintbs:v:20:y:1989:i:3:p:539-552
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    Cited by:

    1. Ana-Maria Fuertes & Elena Kalotychou, 2004. "Elements in the Design of an Early Warning System for Sovereign Default," Computing in Economics and Finance 2004 231, Society for Computational Economics.
    2. Somerville, R. A. & Taffler, R. J., 1995. "Banker judgement versus formal forecasting models: The case of country risk assessment," Journal of Banking & Finance, Elsevier, vol. 19(2), pages 281-297, May.
    3. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "Optimal design of early warning systems for sovereign debt crises," International Journal of Forecasting, Elsevier, vol. 23(1), pages 85-100.
    4. Yucel, Eray, 2011. "A Review and Bibliography of Early Warning Models," MPRA Paper 32893, University Library of Munich, Germany.

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