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

Oliver Linton and Michael Vogt

No 23/12, CeMMAP working papers from Institute for Fiscal Studies

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.

Date: 2012-09-12
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP2312.pdf (application/pdf)

Related works:
Journal Article: Nonparametric estimation of a periodic sequence in the presence of a smooth trend (2014)
Working Paper: Nonparametric estimation of a periodic sequence in the presence of a smooth trend (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:23/12

DOI: 10.1920/wp.cem.2012.2312

Access Statistics for this paper

More papers in CeMMAP working papers from Institute for Fiscal Studies Contact information at EDIRC.
Bibliographic data for series maintained by Dermot Watson ().