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A new quantile regression forecasting model

  • Huarng, Kun-Huang
  • Yu, Tiffany Hui-Kuang
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    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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    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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    1. Granger, C. W. J. & White, Halbert & Kamstra, Mark, 1989. "Interval forecasting : An analysis based upon ARCH-quantile estimators," Journal of Econometrics, Elsevier, vol. 40(1), pages 87-96, January.
    2. Seo, Joo Hwan & Perry, Vanessa G. & Tomczyk, David & Solomon, George T., 2014. "Who benefits most? The effects of managerial assistance on high- versus low-performing small businesses," Journal of Business Research, Elsevier, vol. 67(1), pages 2845-2852.
    3. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    4. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    5. Chan, Nancy Y. C. & Chen, Cathy W.S. & Gerlach, Richard, 2009. "Bayesian time-varying quantile forecasting for Value-at-Risk in financial markets," Working Papers 01/2009, University of Sydney Business School, Discipline of Business Analytics.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Koenker R. & Geling O., 2001. "Reappraising Medfly Longevity: A Quantile Regression Survival Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 458-468, June.
    8. Konrad Banachewicz & André Lucas, 2008. "Quantile forecasting for credit risk management using possibly misspecified hidden Markov models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 566-586.
    9. Yu, Tiffany Hui-Kuang, 2011. "Heterogeneous effects of different factors on global ICT adoption," Journal of Business Research, Elsevier, vol. 64(11), pages 1169-1173.
    10. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-58, March.
    11. Yuzhi Cai & Julian Stander & Neville Davies, 2012. "A new Bayesian approach to quantile autoregressive time series model estimation and forecasting," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(4), pages 684-698, 07.
    12. Martins, Pedro S. & Pereira, Pedro T., 2004. "Does education reduce wage inequality? Quantile regression evidence from 16 countries," Labour Economics, Elsevier, vol. 11(3), pages 355-371, June.
    13. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    14. Conley, Timothy G. & Galenson, David W., 1998. "Nativity and Wealth in Mid-Nineteenth-Century Cities," The Journal of Economic History, Cambridge University Press, vol. 58(02), pages 468-493, June.
    15. Gilbert W. Bassett Jr. & Hsiu-Lang Chen, 2001. "Portfolio style: Return-based attribution using quantile regression," Empirical Economics, Springer, vol. 26(1), pages 293-305.
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