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A new quantile regression forecasting model

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

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.

Suggested Citation

  • Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2014. "A new quantile regression forecasting model," Journal of Business Research, Elsevier, vol. 67(5), pages 779-784.
  • Handle: RePEc:eee:jbrese:v:67:y:2014:i:5:p:779-784
    DOI: 10.1016/j.jbusres.2013.11.044
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    Cited by:

    1. Merigó, José M. & Palacios-Marqués, Daniel & Ribeiro-Navarrete, Belén, 2015. "Aggregation systems for sales forecasting," Journal of Business Research, Elsevier, vol. 68(11), pages 2299-2304.
    2. Cristina Davino & Vincenzo Esposito Vinzi, 2016. "Quantile composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 491-520, December.
    3. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2015. "Healthcare expenditure with causal recipes," Journal of Business Research, Elsevier, vol. 68(7), pages 1570-1573.
    4. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2015. "Forecasting ICT development through quantile confidence intervals," Journal of Business Research, Elsevier, vol. 68(11), pages 2295-2298.
    5. Huarng, Kun-Huang, 2015. "Configural theory for ICT development," Journal of Business Research, Elsevier, vol. 68(4), pages 748-756.
    6. Paniagua, Jordi & Figueiredo, Erik & Sapena, Juan, 2015. "Quantile regression for the FDI gravity equation," Journal of Business Research, Elsevier, vol. 68(7), pages 1512-1518.

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