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Estimation of High Conditional Quantiles for Heavy-Tailed Distributions

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  1. Yi, Yanping & Feng, Xingdong & Huang, Zhuo, 2014. "Estimation of extreme value-at-risk: An EVT approach for quantile GARCH model," Economics Letters, Elsevier, vol. 124(3), pages 378-381.
  2. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
  3. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
  4. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
  5. Daouia, Abdelaati & Stupfler, Gilles & Usseglio-Carleve, Antoine, 2022. "Inference for extremal regression with dependent heavy-tailed data," TSE Working Papers 22-1324, Toulouse School of Economics (TSE), revised 29 Aug 2023.
  6. Yingying Hu & Huixia Judy Wang & Xuming He & Jianhua Guo, 2021. "Bayesian joint-quantile regression," Computational Statistics, Springer, vol. 36(3), pages 2033-2053, September.
  7. Sottile, Gianluca & Frumento, Paolo, 2022. "Robust estimation and regression with parametric quantile functions," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
  8. He, Fengyang & Cheng, Yebin & Tong, Tiejun, 2016. "Estimation of extreme conditional quantiles through an extrapolation of intermediate regression quantiles," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 30-37.
  9. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2022. "Fast algorithms for the quantile regression process," Empirical Economics, Springer, vol. 62(1), pages 7-33, January.
  10. Goedele Dierckx & Yuri Goegebeur & Armelle Guillou, 2021. "Local Robust Estimation of Pareto-Type Tails with Random Right Censoring," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 70-108, February.
  11. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
  12. Stéphane Girard & Gilles Claude Stupfler & Antoine Usseglio-Carleve, 2021. "Extreme Conditional Expectile Estimation in Heavy-Tailed Heteroscedastic Regression Models," Post-Print hal-03306230, HAL.
  13. Huixia Judy Wang & Deyuan Li, 2013. "Estimation of Extreme Conditional Quantiles Through Power Transformation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1062-1074, September.
  14. Goedele Dierckx & Yuri Goegebeur & Armelle Guillou, 2014. "Local robust and asymptotically unbiased estimation of conditional Pareto-type tails," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 330-355, June.
  15. Zhang, Qingzhao & Li, Deyuan & Wang, Hansheng, 2013. "A note on tail dependence regression," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 163-172.
  16. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Jiang, Zhao and Shao's reply to the Discussion of ‘The First Discussion Meeting on Statistical Aspects of the Covid‐19 Pandemic’," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1849-1854, October.
  17. Daisuke Kurisu & Taisuke Otsu, 2021. "Nonparametric inference for extremal conditional quantiles," STICERD - Econometrics Paper Series 616, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  18. Axel Gandy & Kaushik Jana & Almut E. D. Veraart, 2022. "Scoring predictions at extreme quantiles," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 527-544, December.
  19. Kurisu, Daisuke & Otsu, Taisuke, 2023. "Subsampling inference for nonparametric extremal conditional quantiles," LSE Research Online Documents on Economics 120365, London School of Economics and Political Science, LSE Library.
  20. Zhang, Heng-Guo & Su, Chi-Wei & Song, Yan & Qiu, Shuqi & Xiao, Ran & Su, Fei, 2017. "Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model," Economic Modelling, Elsevier, vol. 67(C), pages 355-367.
  21. Tong Siu Tung Wong & Wai Keung Li, 2015. "Extreme values identification in regression using a peaks-over-threshold approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 566-576, March.
  22. Jonathan El Methni & Laurent Gardes & Stéphane Girard, 2014. "Non-parametric Estimation of Extreme Risk Measures from Conditional Heavy-tailed Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 988-1012, December.
  23. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
  24. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
  25. Takuma Yoshida, 2021. "Additive models for extremal quantile regression with Pareto-type distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 103-134, March.
  26. Luke B. Smith & Brian J. Reich & Amy H. Herring & Peter H. Langlois & Montserrat Fuentes, 2015. "Multilevel quantile function modeling with application to birth outcomes," Biometrics, The International Biometric Society, vol. 71(2), pages 508-519, June.
  27. He, Fengyang & Wang, Huixia Judy & Zhou, Yuejin, 2022. "Extremal quantile autoregression for heavy-tailed time series," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
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