Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors
AbstractWe consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate the asymptotic properties. It is shown that the asymptotic properties are affected by both the time dependence and the tail index of the errors. The results of a small simulation study are also given.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Global COE Hi-Stat Discussion Paper Series with number gd10-157.
Date of creation: Dec 2010
Date of revision:
conditional quantile; random design; check function; local linear regression; stable distribution; linear process; long-range dependence; martingale central limit theorem;
Other versions of this item:
- Toshio Honda, 2013. "Nonparametric quantile regression with heavy-tailed and strongly dependent errors," Annals of the Institute of Statistical Mathematics, Springer, vol. 65(1), pages 23-47, February.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Koenker,Roger, 2005.
Cambridge University Press, number 9780521845731.
- Ngai Chan & Rongmao Zhang, 2009. "M-estimation in nonparametric regression under strong dependence and infinite variance," Annals of the Institute of Statistical Mathematics, Springer, vol. 61(2), pages 391-411, June.
- Honda, Toshio, 2006.
"Nonparametric Density Estimation for Linear Processes with Infinite Variance,"
2005-13, Graduate School of Economics, Hitotsubashi University.
- Toshio Honda, 2009. "Nonparametric density estimation for linear processes with infinite variance," Annals of the Institute of Statistical Mathematics, Springer, vol. 61(2), pages 413-439, June.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Honda, Toshio, 2007.
"Nonparametric Estimation of Conditional Medians for Linear and Related Processes,"
2005-04, Graduate School of Economics, Hitotsubashi University.
- Toshio Honda, 2010. "Nonparametric estimation of conditional medians for linear and related processes," Annals of the Institute of Statistical Mathematics, Springer, vol. 62(6), pages 995-1021, December.
- Toshio Honda, 2000. "Nonparametric Estimation of a Conditional Quantile for α-Mixing Processes," Annals of the Institute of Statistical Mathematics, Springer, vol. 52(3), pages 459-470, September.
- Liang Peng & Qiwei Yao, 2004. "Nonparametric regression under dependent errors with infinite variance," Annals of the Institute of Statistical Mathematics, Springer, vol. 56(1), pages 73-86, March.
- Härdle, Wolfgang K. & Song, Song, 2010. "Confidence Bands In Quantile Regression," Econometric Theory, Cambridge University Press, vol. 26(04), pages 1180-1200, August.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tatsuji Makino).
If references are entirely missing, you can add them using this form.