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Smoothing quantile regressions

Citations

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Cited by:

  1. David M. Kaplan, 2022. "Smoothed instrumental variables quantile regression," Stata Journal, StataCorp LLC, vol. 22(2), pages 379-403, June.
  2. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
  3. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
  4. James Mitchell & Aubrey Poon & Dan Zhu, 2024. "Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 790-812, August.
  5. Maria Laura Battagliola & Helle Sørensen & Anders Tolver & Ana-Maria Staicu, 2025. "Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(1), pages 211-230, March.
  6. Tae-Hwy Lee & Aman Ullah & He Wang, 2024. "The second-order bias and mean squared error of quantile regression estimators," Indian Economic Review, Springer, vol. 59(1), pages 11-68, October.
  7. Bartosz Uniejewski, 2023. "Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices," Papers 2302.00411, arXiv.org, revised Nov 2024.
  8. Eduardo Schirmer Finn & Eduardo Horta, 2024. "Convolution Mode Regression," Papers 2412.05736, arXiv.org.
  9. Jean-Jacques Forneron, 2023. "Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models," Papers 2301.07196, arXiv.org, revised Aug 2025.
  10. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
  11. Lee, Sokbae & Liao, Yuan & Seo, Myung Hwan & Shin, Youngki, 2025. "Fast inference for quantile regression with tens of millions of observations," Journal of Econometrics, Elsevier, vol. 249(PA).
  12. Songnian Chen & Junlong Feng, 2025. "Universal Factor Models," Papers 2501.15761, arXiv.org, revised Jul 2025.
  13. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
  14. Kean Ming Tan & Lan Wang & Wen‐Xin Zhou, 2022. "High‐dimensional quantile regression: Convolution smoothing and concave regularization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 205-233, February.
  15. Abankwah, Stephen Asare & Afriyie, Samuel Osei, 2025. "Modelling Sustainable Energy Transition in BRICS+ Countries: A Smoothed Common Correlated Effects Instrumental Variable Quantile Regression Approach," MPRA Paper 123758, University Library of Munich, Germany.
  16. Zhaohan Hou & Wei Ma & Lei Wang, 2023. "Sparse and debiased lasso estimation and inference for high-dimensional composite quantile regression with distributed data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1230-1250, December.
  17. Mario Forni & Luca Gambetti & Nicolò Maffei-Faccioli & Luca Sala, 2023. "The impact of financial shocks on the forecast distribution of output and inflation," Working Paper 2023/3, Norges Bank.
  18. Tomasz Serafin & Bartosz Uniejewski, 2024. "Ranking probabilistic forecasting models with different loss functions," Papers 2411.17743, arXiv.org.
  19. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
  20. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
  21. Rong Jiang & Siu Kai Choy & Keming Yu, 2024. "Non‐crossing quantile double‐autoregression for the analysis of streaming time series data," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 513-532, July.
  22. Tae-Hwy Lee & Aman Ullah & He Wang, 2023. "The Second-order Bias and Mean Squared Error of Quantile Regression Estimators," Working Papers 202313, University of California at Riverside, Department of Economics.
  23. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2023. "Smoothing the Nonsmoothness," Papers 2309.16348, arXiv.org.
  24. Venkatram Kari & Geetha Mary Amalanathan, 2025. "Targeted prevention of risky deals for improper granular data with deep learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(2), pages 750-764, February.
  25. Forni, Mario & Gambetti, Luca & Maffei-Faccioli, Nicolò & Sala, Luca, 2024. "The effects of monetary policy on macroeconomic risk," European Economic Review, Elsevier, vol. 167(C).
  26. Ufuk Beyaztas & Han Lin Shang & Semanur Saricam, 2025. "Penalized function-on-function linear quantile regression," Computational Statistics, Springer, vol. 40(1), pages 301-329, January.
  27. Xie, Jinhan & Yan, Xiaodong & Jiang, Bei & Kong, Linglong, 2025. "Statistical inference for smoothed quantile regression with streaming data," Journal of Econometrics, Elsevier, vol. 249(PA).
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