A new quantile regression forecasting model

Kun-Huang Huarng and Tiffany Hui-Kuang Yu

Journal of Business Research, 2014, vol. 67, issue 5, 779-784

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.

Keywords: Health care expenditure; ICT; New quantile information criterion; Forecasting (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0148296313004141
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:67:y:2014:i:5:p:779-784

DOI: 10.1016/j.jbusres.2013.11.044

Access Statistics for this article

Journal of Business Research is currently edited by A. G. Woodside

More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().