IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

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

  • Miguel Ángel Bermejo

    ()

  • Daniel Peña

    ()

  • Ismael Sánchez

    ()

Registered author(s):

    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

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://e-archivo.uc3m.es/bitstream/10016/11688/1/ws111813.pdf
    Download Restriction: no

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws111813.

    as
    in new window

    Length:
    Date of creation: Jun 2011
    Date of revision:
    Handle: RePEc:cte:wsrepe:ws111813
    Contact details of provider: Postal: C/ Madrid, 126 - 28903 GETAFE (MADRID)
    Phone: 6249847
    Fax: 6249849
    Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica
    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223.
    2. 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.
    3. Valentina Corradi & Norman Swanson, 2004. "Predictive Density Evaluation," Departmental Working Papers 200419, Rutgers University, Department of Economics.
    4. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    5. Valentina Corradi & Norman Swanson, 2006. "Predictive Density Evaluation. Revised," Departmental Working Papers 200621, Rutgers University, Department of Economics.
    6. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
    7. 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.
    8. Gloria González-Rivera & Zeynep Senyuz & Emre Yoldas, 2011. "Autocontours: Dynamic Specification Testing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 186-200, January.
    9. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:ws111813. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.