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Densidad de predicción basada en momentos condicionados y máxima entropía : aplicación a la predicción de potencia eólica

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  • Peña Sánchez de Rivera, Daniel
  • Bermejo Mancera, Miguel Ángel
  • Sánchez, Ismael

Abstract

El cálculo de predicciones puntuales junto con su incertidumbre en forma de intervalo es, en la mayoría de aplicaciones, insuficiente. Especialmente cuando estemos asumiendo no linealidad en los datos, puesto que en estos casos, podrían existir incluso cambios en la distribución. Por ello será necesario, además de la predicción puntual, obtener una estimación de la densidad condicionada de la variable en el futuro dado su comportamiento actual, es decir, la densidad predictiva. En este trabajo proponemos una estimación de la densidad predictiva empleando diferentes distribuciones paramétricas como son la Normal Truncada, la Normal Censurada, la Beta y la de Máxima Entropía. Dichas distribuciones serán calculadas empleando los momentos condicionados estimados mediante un método de estimación recursiva. Se aplica el procedimiento a datos provenientes de energía eólica

Suggested Citation

  • Peña Sánchez de Rivera, Daniel & Bermejo Mancera, Miguel Ángel & Sánchez, Ismael, 2011. "Densidad de predicción basada en momentos condicionados y máxima entropía : aplicación a la predicción de potencia eólica," DES - Working Papers. Statistics and Econometrics. WS ws111813, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws111813
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    References listed on IDEAS

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    1. González-Rivera, Gloria & Senyuz, Zeynep & Yoldas, Emre, 2011. "Autocontours: Dynamic Specification Testing," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 186-200.
    2. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    3. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    4. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    5. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223, April.
    6. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    7. Valentina Corradi & Norman Swanson, 2006. "Predictive Density Evaluation. Revised," Departmental Working Papers 200621, Rutgers University, Department of Economics.
    8. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    9. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    10. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive Density Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 5, pages 197-284, Elsevier.
    11. Moller, Jan Kloppenborg & Nielsen, Henrik Aalborg & Madsen, Henrik, 2008. "Time-adaptive quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1292-1303, January.
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    Keywords

    Potencia eólica;

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