IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/ws032104.html
   My bibliography  Save this paper

A bayesian approach for predicting with polynomial regresión of unknown degree

Author

Listed:
  • Guttman, Irwin
  • Peña, Daniel
  • Redondas, María Dolores

Abstract

This article presents a comparison of four methods to compute the posterior probabilities of the possible orders in polynomial regression models. These posterior probabilities are used for forecasting by using Bayesian model averaging. It is shown that Bayesian model averaging provides a closer relationship between the theoretical coverage of the high density predictive interval (HDPI) and the observed coverage than those corresponding to selecting the best model. The performance of the different procedures are illustrated with simulations and some known engineering data.

Suggested Citation

  • Guttman, Irwin & Peña, Daniel & Redondas, María Dolores, 2003. "A bayesian approach for predicting with polynomial regresión of unknown degree," DES - Working Papers. Statistics and Econometrics. WS ws032104, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws032104
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/b83ae80c-5693-4bb1-8b17-8a97de89cb71/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Philips, R. & Guttman, I., 1998. "A new criterion for variable selection," Statistics & Probability Letters, Elsevier, vol. 38(1), pages 11-19, May.
    2. Carmen Fernández & Eduardo Ley & Mark F. J. Steel, 2002. "Bayesian modelling of catch in a north‐west Atlantic fishery," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 257-280, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Łukasz Jarosław Kozar & Robert Matusiak & Marta Paduszyńska & Adam Sulich, 2022. "Green Jobs in the EU Renewable Energy Sector: Quantile Regression Approach," Energies, MDPI, vol. 15(18), pages 1-21, September.
    2. Durlauf, Steven N. & Navarro, Salvador & Rivers, David A., 2016. "Model uncertainty and the effect of shall-issue right-to-carry laws on crime," European Economic Review, Elsevier, vol. 81(C), pages 32-67.
    3. Yiyun Shou & Michael Smithson, 2015. "Evaluating Predictors of Dispersion: A Comparison of Dominance Analysis and Bayesian Model Averaging," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 236-256, March.
    4. Babatunde Abidoye & Joseph Herriges, 2012. "Model Uncertainty in Characterizing Recreation Demand," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 53(2), pages 251-277, October.
    5. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    6. Arnoldo Frigessi & Marit Holden & Clare Marshall & Hildegunn Viljugrein & Nils Chr. Stenseth & Lars Holden & Vladimir Ageyev & Nikolay L. Klassovskiy, 2005. "Bayesian Population Dynamics of Interacting Species: Great Gerbils and Fleas in Kazakhstan," Biometrics, The International Biometric Society, vol. 61(1), pages 230-238, March.
    7. Gilles Celeux & Mohammed El Anbari & Jean-Michel Marin & Christian P. Robert, 2010. "Regularization in Regression : Comparing Bayesian and Frequentist Methods in a Poorly Informative Situation," Working Papers 2010-43, Center for Research in Economics and Statistics.
    8. Adam Sulich & Letycja Sołoducho-Pelc, 2022. "Changes in Energy Sector Strategies: A Literature Review," Energies, MDPI, vol. 15(19), pages 1-26, September.
    9. Theo Eicher & Jeff Begun, 2008. "In Search of a Sulphur Dioxide Environmental Kuznets Curve: A Bayesian Model Averaging Approach," Working Papers UWEC-2007-19-P, University of Washington, Department of Economics.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:ws032104. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.