Package: esemifar
Type: Package
Title: Smoothing Long-Memory Time Series
Version: 1.0.1
Description: 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>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: fracdiff, stats, smoots, graphics, grDevices
Depends: R (>= 2.10)
Authors@R:
c(person("Yuanhua", "Feng", role = "aut",
comment = "Paderborn University, Germany"),
person("Jan", "Beran", role = "aut",
comment = "University of Konstanz, Germany"),
person("Sebastian", "Letmathe", role = c("aut", "cre"),
email = "sebastian.letmathe@uni-paderborn.de",
comment = "Paderborn University, Germany"),
person("Dominik", "Schulz", role = c("aut"),
comment = "Paderborn University, Germany"))
URL: https://wiwi.uni-paderborn.de/en/dep4/feng/
Acknowledgments: This work was supported by the German DFG project
GZ-FE-1500-2-1.
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-11-06 10:10:54 UTC; Letmode
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@uni-paderborn.de>
Repository: CRAN
Date/Publication: 2021-11-06 10:20:02 UTC

Description: Computes shrinkage estimators for regression problems. Selects
penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution.

penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) <doi:10.1093/aje/kwx225> for more details.