Advanced Search
MyIDEAS: Login

Asymptotic normality of regression estimators with long memory errors

Contents:

Author Info

  • Giraitis, Liudas
  • Koul, Hira L.
  • Surgailis, Donatas

Abstract

This paper discusses asymptotic normality of certain classes of M- and R-estimators of the slope parameter vector in linear regression models with long memory moving average errors, extending recent results of Koul (1992) and Koul and Mukherjee (1993). Like in the case of the long memory Gaussian errors, it is observed that all these estimators are asymptotically equivalent to the least squares estimator, a fact that is in sharp contrast with the i.i.d. errors case.

Download Info

If 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.
File URL: http://www.sciencedirect.com/science/article/B6V1D-3VVCJJ8-1D/2/ebf56084d423ea5485468bd00255044d
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 29 (1996)
Issue (Month): 4 (September)
Pages: 317-335

as in new window
Handle: RePEc:eee:stapro:v:29:y:1996:i:4:p:317-335

Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description

Order Information:
Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
Web: https://shop.elsevier.com/order?id=505573&ref=505573_01_ooc_1&version=01

Related research

Keywords: M-; R-estimators Appell polynomials Weighted empiricals;

References

References listed on IDEAS
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.:
as in new window
  1. Koul, Hira L., 1992. "M-estimators in linear models with long range dependent errors," Statistics & Probability Letters, Elsevier, vol. 14(2), pages 153-164, May.
Full references (including those not matched with items on IDEAS)

Citations

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

Cited by:
  1. Magdalinos, Tassos, 2012. "Mildly explosive autoregression under weak and strong dependence," Journal of Econometrics, Elsevier, vol. 169(2), pages 179-187.
  2. Comte, F. & Merlevède, F., 2005. "Super optimal rates for nonparametric density estimation via projection estimators," Stochastic Processes and their Applications, Elsevier, vol. 115(5), pages 797-826, May.
  3. Honda, Toshio, 2007. "Nonparametric Estimation of Conditional Medians for Linear and Related Processes," Discussion Papers 2005-04, Graduate School of Economics, Hitotsubashi University.
  4. Honda, Toshio, 2006. "Nonparametric Density Estimation for Linear Processes with Infinite Variance," Discussion Papers 2005-13, Graduate School of Economics, Hitotsubashi University.
  5. Beran, Jan & Feng, Yuanhua & Ghosh, Sucharita & Sibbertsen, Philipp, 2002. "On robust local polynomial estimation with long-memory errors," International Journal of Forecasting, Elsevier, vol. 18(2), pages 227-241.
  6. Hira Koul & Nao Mimoto & Donatas Surgailis, 2013. "Goodness-of-fit tests for long memory moving average marginal density," Metrika, Springer, vol. 76(2), pages 205-224, February.
  7. Ould Haye, Mohamedou & Philippe, Anne, 2011. "Marginal density estimation for linear processes with cyclical long memory," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1354-1364, September.
  8. Youndjé, É. & Vieu, P., 2006. "A note on quantile estimation for long-range dependent stochastic processes," Statistics & Probability Letters, Elsevier, vol. 76(2), pages 109-116, January.
  9. Mohamed Boutahar, 2006. "Limiting distribution of the least squaresestimates in polynomial regression with longmemory noises," Working Papers halshs-00409571, HAL.
  10. Surgailis, Donatas, 0. "Stable limits of empirical processes of moving averages with infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 100(1-2), pages 255-274, July.
  11. Koul, Hira L. & Baillie, Richard T., 2003. "Asymptotics of M-estimators in non-linear regression with long memory designs," Statistics & Probability Letters, Elsevier, vol. 61(3), pages 237-252, February.
  12. Tassos Magdalinos, 2008. "Mildly explosive autoregression under weak and strong dependence," Discussion Papers 08/05, University of Nottingham, Granger Centre for Time Series Econometrics.
  13. Beran, Jan, 2006. "On location estimation for LARCH processes," Journal of Multivariate Analysis, Elsevier, vol. 97(8), pages 1766-1782, September.
  14. Lorek, Paweł & Kulik, Rafał, 2014. "Empirical process of residuals for regression models with long memory errors," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 7-16.
  15. Hira Koul & Donatas Surgailis, 2000. "Asymptotic Normality of the Whittle Estimator in Linear Regression Models with Long Memory Errors," Statistical Inference for Stochastic Processes, Springer, vol. 3(1), pages 129-147, January.
  16. Zhao, Zhibiao & Wu, Wei Biao, 2007. "Asymptotic theory for curve-crossing analysis," Stochastic Processes and their Applications, Elsevier, vol. 117(7), pages 862-877, July.
  17. Lihong Wang, 2004. "Asymptotics of estimates in constrained nonlinear regression with long-range dependent innovations," Annals of the Institute of Statistical Mathematics, Springer, vol. 56(2), pages 251-264, June.
  18. Koul, Hira L. & Surgailis, Donatas, 2001. "Asymptotics of empirical processes of long memory moving averages with infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 91(2), pages 309-336, February.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:29:y:1996:i:4:p:317-335. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.