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Time-varying quantile single-index model for multivariate responses

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  • Zhao, Weihua
  • Zhou, Yan
  • Lian, Heng

Abstract

We consider simultaneous semiparametric estimation of conditional quantiles for multiple responses using a dynamic single-index structure. Motivated by a financial application, a market factor index is constructed that is shared among different portfolios which results in a more interpretable and efficient model, compared to separately building multiple conditional quantiles. On the other hand, the link functions are allowed to be different across portfolios. The asymptotic normality of the index parameter is established, as well as the convergence rate of the nonparametric functions. Monte Carlo studies demonstrated the advantages of the proposed estimator and an application to financial data is used to illustrate the method.

Suggested Citation

  • Zhao, Weihua & Zhou, Yan & Lian, Heng, 2018. "Time-varying quantile single-index model for multivariate responses," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 32-49.
    • Handle: RePEc:eee:csdana:v:127:y:2018:i:c:p:32-49
    DOI: 10.1016/j.csda.2018.05.006
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    References listed on IDEAS

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    1. Wu, Tracy Z. & Yu, Keming & Yu, Yan, 2010. "Single-index quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1607-1621, August.
    2. Yu Y. & Ruppert D., 2002. "Penalized Spline Estimation for Partially Linear Single-Index Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1042-1054, December.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. He, Xuming & Shi, Peide, 1996. "Bivariate Tensor-Product B-Splines in a Partly Linear Model," Journal of Multivariate Analysis, Elsevier, vol. 58(2), pages 162-181, August.
    5. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    6. He, Qianchuan & Kong, Linglong & Wang, Yanhua & Wang, Sijian & Chan, Timothy A. & Holland, Eric, 2016. "Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 222-239.
    7. Tsai, I-Chun, 2012. "The relationship between stock price index and exchange rate in Asian markets: A quantile regression approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 609-621.
    8. Cai, Zongwu & Xiao, Zhijie, 2012. "Semiparametric quantile regression estimation in dynamic models with partially varying coefficients," Journal of Econometrics, Elsevier, vol. 167(2), pages 413-425.
    9. Kong, Efang & Xia, Yingcun, 2012. "A Single-Index Quantile Regression Model And Its Estimation," Econometric Theory, Cambridge University Press, vol. 28(4), pages 730-768, August.
    10. Cai, Zongwu & Juhl, Ted & Yang, Bingduo, 2015. "Functional index coefficient models with variable selection," Journal of Econometrics, Elsevier, vol. 189(2), pages 272-284.
    11. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    12. Xiaoke Zhang & Byeong U. Park & Jane-ling Wang, 2013. "Time-Varying Additive Models for Longitudinal Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 983-998, September.
    13. Christou, Eliana & Akritas, Michael G., 2016. "Single index quantile regression for heteroscedastic data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 169-182.
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