Advanced Search
MyIDEAS: Login

Nonparametric estimation of a periodic sequence in the presence of a smooth trend

Contents:

Author Info

  • Michael Vogt
  • Oliver Linton

    ()
    (Institute for Fiscal Studies and Cambridge University)

Abstract

In this paper, we study a nonparametric regression model including a periodic component, a smooth trend function, and a stochastic error term. We propose a procedure to estimate the unknown period and the function values of the periodic component as well as the nonparametric trend function. The theoretical part of the paper establishes the asymptotic properties of our estimators. In particular, we show that our estimator of the period is consistent. In addition, we derive the convergence rates as well as the limiting distributions of our estimators of the periodic component and the trend function. The asymptotic results are complemented with a simulation study that investigates the small sample behaviour of our procedure. Finally, we illustrate our method by applying it to a series of global temperature anomalies.

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.cemmap.ac.uk/wps/cwp231212.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP23/12.

as in new window
Length:
Date of creation: Sep 2012
Date of revision:
Handle: RePEc:ifs:cemmap:23/12

Contact details of provider:
Postal: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE
Phone: (+44) 020 7291 4800
Fax: (+44) 020 7323 4780
Email:
Web page: http://cemmap.ifs.org.uk
More information through EDIRC

Order Information:
Postal: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE
Email:

Related research

Keywords: Nonparametric estimation; penalized least squares; periodic sequence; temperature anomaly data.;

This paper has been announced in the following NEP Reports:

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. Marc G. Genton & Peter Hall, 2007. "Statistical inference for evolving periodic functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 643-657.
  2. Peter Hall & Ming Li, 2006. "Using the periodogram to estimate period in nonparametric regression," Biometrika, Biometrika Trust, vol. 93(2), pages 411-424, June.
  3. Peter Hall & Jiying Yin, 2003. "Nonparametric methods for deconvolving multiperiodic functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 869-886.
  4. Robert M. De Jong & James Davidson, 2000. "Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices," Econometrica, Econometric Society, vol. 68(2), pages 407-424, March.
  5. Kristensen, Dennis, 2009. "Uniform Convergence Rates Of Kernel Estimators With Heterogeneous Dependent Data," Econometric Theory, Cambridge University Press, vol. 25(05), pages 1433-1445, October.
Full references (including those not matched with items on IDEAS)

Citations

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:ifs:cemmap:23/12. 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: (Stephanie Seavers).

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.