Next Article in Journal
Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics
Next Article in Special Issue
Filters, Waves and Spectra
Previous Article in Journal
Econometric Fine Art Valuation by Combining Hedonic and Repeat-Sales Information
Open AccessArticle

Some Results on 1 Polynomial Trend Filtering

Graduate School of Social Sciences, Hiroshima University, 1-2-1 Kagamiyama, Higashi-Hiroshima 739-8525, Japan
*
Author to whom correspondence should be addressed.
Econometrics 2018, 6(3), 33; https://doi.org/10.3390/econometrics6030033
Received: 22 May 2018 / Revised: 30 June 2018 / Accepted: 4 July 2018 / Published: 10 July 2018
(This article belongs to the Special Issue Filtering)
1 polynomial trend filtering, which is a filtering method described as an 1-norm penalized least-squares problem, is promising because it enables the estimation of a piecewise polynomial trend in a univariate economic time series without prespecifying the number and location of knots. This paper shows some theoretical results on the filtering, one of which is that a small modification of the filtering provides not only identical trend estimates as the filtering but also extrapolations of the trend beyond both sample limits. View Full-Text
Keywords: ℓ1 trend filtering; Hodrick–Prescott filtering; Whittaker–Henderson method of graduation; Lasso regression; basis pursuit denoising; total variation denoising ℓ1 trend filtering; Hodrick–Prescott filtering; Whittaker–Henderson method of graduation; Lasso regression; basis pursuit denoising; total variation denoising
MDPI and ACS Style

Yamada, H.; Du, R. Some Results on 1 Polynomial Trend Filtering. Econometrics 2018, 6, 33.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop