Journal article
Smoothing Time Series with Local Polynomial Regression on Time
Communications in statistics. Theory and methods, Vol.37(6), pp.959-971
02/11/2008
DOI: 10.1080/03610920701693843
Abstract
Time series smoothers estimate the level of a time series at time t as its conditional expectation given present, past and future observations, with the smoothed value depending on the estimated time series model. Alternatively, local polynomial regressions on time can be used to estimate the level, with the implied smoothed value depending on the weight function and the bandwidth in the local linear least squares fit. In this article we compare the two smoothing approaches and describe their similarities. Through simulations, we assess the increase in the mean square error that results when approximating the estimated optimal time series smoother with the local regression estimate of the level.
Details
- Title: Subtitle
- Smoothing Time Series with Local Polynomial Regression on Time
- Creators
- Johannes Ledolter - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Communications in statistics. Theory and methods, Vol.37(6), pp.959-971
- Publisher
- Taylor & Francis Group
- DOI
- 10.1080/03610920701693843
- ISSN
- 0361-0926
- eISSN
- 1532-415X
- Language
- English
- Date published
- 02/11/2008
- Academic Unit
- Statistics and Actuarial Science; Business Analytics
- Record Identifier
- 9984380512702771
Metrics
11 Record Views