esemifar: Smoothing Long-Memory Time Series

The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2021) <https://ideas.repec.org/p/pdn/ciepap/145.html>.

Version:	1.0.1
Depends:	R (≥ 2.10)
Imports:	fracdiff, stats, smoots, graphics, grDevices
Published:	2021-11-06
Author:	Yuanhua Feng [aut] (Paderborn University, Germany), Jan Beran [aut] (University of Konstanz, Germany), Sebastian Letmathe [aut, cre] (Paderborn University, Germany), Dominik Schulz [aut] (Paderborn University, Germany)
Maintainer:	Sebastian Letmathe <sebastian.letmathe at uni-paderborn.de>
License:	GPL-3
URL:	https://wiwi.uni-paderborn.de/en/dep4/feng/
NeedsCompilation:	no
Materials:	README NEWS
In views:	TimeSeries
CRAN checks:	esemifar results

Documentation:

Reference manual:

esemifar.pdf

Downloads:

Package source:	esemifar_1.0.1.tar.gz
Windows binaries:	r-devel: esemifar_1.0.1.zip, r-release: esemifar_1.0.1.zip, r-oldrel: esemifar_1.0.1.zip
macOS binaries:	r-release (arm64): esemifar_1.0.1.tgz, r-oldrel (arm64): esemifar_1.0.1.tgz, r-release (x86_64): esemifar_1.0.1.tgz, r-oldrel (x86_64): esemifar_1.0.1.tgz
Old sources:	esemifar archive

Reverse dependencies:

Reverse imports:

ufRisk

Linking:

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