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A new quantile regression forecasting model

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  • Huarng, Kun-Huang
  • Yu, Tiffany Hui-Kuang
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    Abstract

    Quantile regression is popular because it provides more information as well as comprehensive interpretations. To improve forecasting performance, this study proposes a new quantile information criterion (NQIC), on the basis of the coefficient of variation, and expects the NQIC to reflect whether a variable is predictable. The health care expenditure data determine the thresholds for the NQICs. The thresholds assist in forecasting the development of information and communication technology. From the empirical analyses, the NQICs and thresholds greatly improve the forecasting performance.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0148296313004141
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Business Research.

    Volume (Year): 67 (2014)
    Issue (Month): 5 ()
    Pages: 779-784

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    Handle: RePEc:eee:jbrese:v:67:y:2014:i:5:p:779-784

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    Web page: http://www.elsevier.com/locate/jbusres

    Related research

    Keywords: Health care expenditure; ICT; New quantile information criterion; Forecasting;

    References

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