IDEAS home Printed from https://ideas.repec.org/r/oup/biomet/v98y2011i4p995-999.html
   My bibliography  Save this item

Wild bootstrap for quantile regression

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
  2. Daniel Gutknecht & Stefan Hoderlein & Michael Peters, 2014. "Costly Information Processing and Income Expectations," Boston College Working Papers in Economics 861, Boston College Department of Economics.
  3. Tadao Hoshino, 2014. "Quantile regression estimation of partially linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(3), pages 509-536, September.
  4. Souza, Tatiene C. & Cribari–Neto, Francisco, 2018. "Intelligence and religious disbelief in the United States," Intelligence, Elsevier, vol. 68(C), pages 48-57.
  5. Xiaofeng Lv & Rui Li, 2013. "Smoothed empirical likelihood analysis of partially linear quantile regression models with missing response variables," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 317-347, October.
  6. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
  7. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
  8. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
  9. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
  10. Rojas-Perilla, Natalia & Pannier, Sören & Schmid, Timo & Tzavidis, Nikos, 2017. "Data-driven transformations in small area estimation," Discussion Papers 2017/30, Free University Berlin, School of Business & Economics.
  11. Sarantis Lolos & Panagiotis Palaios & Evangelia Papapetrou, 2023. "Tourism-led growth asymmetries in Greece: evidence from quantile regression analysis," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(1), pages 125-148, January.
  12. Syed Jawad Hussain Shahzad & Dene Hurley & Román Ferrer, 2021. "U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3569-3587, July.
  13. Valérie Mignon & Jamel Saadaoui, 2022. "Asymmetries in the oil market: Accounting for the growing role of China through quantile regressions," Working Papers of BETA 2022-36, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  14. Antonio F. Galvao & Gabriel Montes-Rojas, 2015. "On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study," Econometrics, MDPI, vol. 3(3), pages 1-13, September.
  15. Malikov, Emir & Hartarska, Valentina & Mersland, Roy, 2020. "Economies of diversification in microfinance: Evidence from quantile estimation on panel data," Finance Research Letters, Elsevier, vol. 34(C).
  16. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2024. "Bootstrap Inference for Panel Data Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 628-639, April.
  17. Andreas Hagemann, 2017. "Cluster-Robust Bootstrap Inference in Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 446-456, January.
  18. Nakagawa, Kei & Sakemoto, Ryuta, 2022. "Market uncertainty and correlation between Bitcoin and Ether," Finance Research Letters, Elsevier, vol. 50(C).
  19. Xiaofeng Lv & Gupeng Zhang & Xinkuo Xu & Qinghai Li, 2019. "Weighted quantile regression for censored data with application to export duration data," Statistical Papers, Springer, vol. 60(4), pages 1161-1192, August.
  20. Vadim Volkov, 2016. "Legal and Extralegal Origins of Sentencing Disparities: Evidence from Russia's Criminal Courts," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 13(4), pages 637-665, December.
  21. repec:hum:wpaper:sfb649dp2014-028 is not listed on IDEAS
  22. M. Tamilselvan & Srinivasan Palamalai & Magesh Kumar & Bipasha Maity & Nidhi Agrawal, 2022. "Electricity Demand and CO Emissions during the COVID-19 Pandemic: The Case of India," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 161-169, May.
  23. Holger Dette & Matthias Guhlich & Natalie Neumeyer, 2015. "Testing for additivity in nonparametric quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 437-477, June.
  24. Shih-Kang Chao & Katharina Proksch & Holger Dette & Wolfgang Karl Härdle, 2017. "Confidence Corridors for Multivariate Generalized Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 70-85, January.
  25. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
  26. Su, Liangjun & Hoshino, Tadao, 2016. "Sieve instrumental variable quantile regression estimation of functional coefficient models," Journal of Econometrics, Elsevier, vol. 191(1), pages 231-254.
  27. Jan Beyersmann & Susanna Di Termini & Markus Pauly, 2013. "Weak Convergence of the Wild Bootstrap for the Aalen–Johansen Estimator of the Cumulative Incidence Function of a Competing Risk," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 387-402, September.
  28. Lamarche, Carlos & Parker, Thomas, 2023. "Wild bootstrap inference for penalized quantile regression for longitudinal data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1799-1826.
  29. Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.
  30. J. C. Escanciano & S. C. Goh, 2019. "Quantile-Regression Inference With Adaptive Control of Size," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1382-1393, July.
  31. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.
  32. Adam Maidman & Lan Wang, 2018. "New semiparametric method for predicting high‐cost patients," Biometrics, The International Biometric Society, vol. 74(3), pages 1104-1111, September.
  33. Nicholas Apergis, 2022. "Evaluating tail risks for the U.S. economic policy uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3971-3989, October.
  34. Natalia Rojas‐Perilla & Sören Pannier & Timo Schmid & Nikos Tzavidis, 2020. "Data‐driven transformations in small area estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 121-148, January.
  35. Balcilar, Mehmet & Gupta, Rangan & Nel, Jacobus, 2022. "Rare disaster risks and gold over 700 years: Evidence from nonparametric quantile regressions," Resources Policy, Elsevier, vol. 79(C).
  36. Daniel Gutknecht & Stefan Hoderlein & Michael Peters, 2016. "Constrained Information Processing and Individual Income Expectations," Boston College Working Papers in Economics 898, Boston College Department of Economics.
  37. Bonaccolto, Giovanni & Borri, Nicola & Consiglio, Andrea, 2023. "Breakup and default risks in the great lockdown," Journal of Banking & Finance, Elsevier, vol. 147(C).
  38. Liu, Jicai & Si, Yuefeng & Niu, Yong & Zhang, Riquan, 2022. "Projection quantile correlation and its use in high-dimensional grouped variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
  39. Luca Greco & George Luta & Rand Wilcox, 2024. "On testing the equality between interquartile ranges," Computational Statistics, Springer, vol. 39(5), pages 2873-2898, July.
  40. Reboredo, Juan C. & Ugolini, Andrea, 2024. "The impact of uncertainty shocks on energy transition metal prices," Resources Policy, Elsevier, vol. 95(C).
  41. Feng, Xingdong & Li, Wenyu & Zhu, Qianqian, 2024. "Estimation and bootstrapping under spatiotemporal models with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
  42. Guodong Li & Yang Li & Chih-Ling Tsai, 2015. "Quantile Correlations and Quantile Autoregressive Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 246-261, March.
  43. Xiaowen Dai & Shidan Huang & Libin Jin & Maozai Tian, 2023. "Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
  44. Ahmed Imran Hunjra & Muhammad Azam & Mamdouh Abdulaziz Saleh Al‐Faryan, 2024. "The nexus between climate change risk and financial policy uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1401-1416, April.
  45. Weidenhammer, Beate & Schmid, Timo & Salvati, Nicola & Tzavidis, Nikos, 2016. "A unit-level quantile nested error regression model for domain prediction with continuous and discrete outcomes," Discussion Papers 2016/12, Free University Berlin, School of Business & Economics.
  46. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.
  47. Ryuta Sakemoto, 2018. "The intertemporal relation between expected returns and conditional correlations between precious metals and the stock market," Economics and Business Letters, Oviedo University Press, vol. 7(1), pages 24-35.
  48. 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.
  49. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.
  50. Marc Ditzhaus & Roland Fried & Markus Pauly, 2021. "QANOVA: quantile-based permutation methods for general factorial designs," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 960-979, December.
  51. Maistre, Samuel & Lavergne, Pascal & Patilea, Valentin, 2014. "Powerful nonparametric checks for quantile regression," TSE Working Papers 14-501, Toulouse School of Economics (TSE).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.