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Combining forecasts to enhance fish production prediction: the Case of Coastal Fish Production in Morocco

Author

Listed:
  • David Bouras

    (Lincoln University, Missouri, EEUU.)

Abstract

This paper seeks to enhance forecast accuracy by combining three individual forecasting models. These models include: the Autoregressive Integrated Moving Average model (ARIMA), the Generalized Autoregressive Conditional Heteroscedastic model (GARCH), and the Census X11 model. Applied to the Moroccan coastal fish production, the empirical results show that in terms of predictive ability the composite model outperforms the individual forecasting models. In addition, the results reveal that the forecast accuracy gains arising from combining the individual forecasts range from nearly 8% to over 95%.

Suggested Citation

  • David Bouras, 2015. "Combining forecasts to enhance fish production prediction: the Case of Coastal Fish Production in Morocco," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 1, pages 1-1, December.
    • Handle: RePEc:eac:articl:10/14
    as

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    References listed on IDEAS

    as
    1. Newbold, Paul & Zumwalt, J. Kenton & Kannan, Srinivasan, 1987. "Combining forecasts to improve earnings per share prediction : An examination of electric utilities," International Journal of Forecasting, Elsevier, vol. 3(2), pages 229-238.
    2. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. Wolff, Matthias & Taylor, Marc & Mendo, Jaime & Yamashiro, Carmen, 2007. "A catch forecast model for the Peruvian scallop (Argopecten purpuratus) based on estimators of spawning stock and settlement rate," Ecological Modelling, Elsevier, vol. 209(2), pages 333-341.
    8. Stephen H. Penman, 1998. "Combining Earnings and Book Value in Equity Valuation," Contemporary Accounting Research, John Wiley & Sons, vol. 15(3), pages 291-324, September.
    9. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    10. Engle, Robert F. & Granger, C. W. J. & Kraft, Dennis, 1984. "Combining competing forecasts of inflation using a bivariate arch model," Journal of Economic Dynamics and Control, Elsevier, vol. 8(2), pages 151-165, November.
    Full references (including those not matched with items on IDEAS)

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