esemifar {esemifar} | R Documentation |
esemifar: A package for data-driven nonparametric estimation of the trend and its derivatives in equidistant time series.
Description
The esemifar
package provides different applicable functions for the
estimation of the trend or its derivatives in equidistant time series.
The main functions include an automated bandwidth selection method for time
series with long-memory errors.
Functions (version 1.0.0)
The esemifar
functions are either meant for calculating nonparametric
estimates of the trend of a time series or its derivatives.
dsmoothlm
is a function that calculates the derivatives of the
trend after obtaining the optimal bandwidth by an iterative plug-in
algorithm.
tsmoothlm
is the central function of the package. It allows
the user to conduct a local polynomial regression of the trend based on
an optimal bandwidth that is obtained by an iterative plug-in algorithm.
Inflation rate (and other factors) can be manually
and individually adjusted as arguments in the function
(see also: tsmoothlm
).
critMatlm
is a quick tool for the calculation of information criteria
for FARIMA(p,d,q
) models with different order combinations p
and
q
. The function returns a matrix with the obtained values of the
selected criterion for the different combinations of p
and q
(see also: critMatlm
).
Datasets
The package includes two datasets: airLDN
(see also:
airLDN
) with daily observations of individual air pollutants
from 2014 to 2020 and gdpG7
(see also: gdpG7
) that has
data concerning the quarterly G7 GDP between Q1 1962 and Q4 2019.
License
The package is distributed under the General Public License v3 ([GPL-3](https://tldrlegal.com/license/gnu-general-public-license-v3-(gpl-3))).
Author(s)
Yuanhua Feng (Department of Economics, Paderborn University),
Author of the Algorithms
Website: https://wiwi.uni-paderborn.de/en/dep4/feng/Sebastian Letmathe,
Package Creator and Maintainer
References
Beran, J. and Y. Feng (2002a). Iterative plug-in algorithms for SEMIFAR models - definition, convergence, and asymptotic properties. Journal of Computational and Graphical Statistics 11(3), 690-713.
Beran, J. and Feng, Y. (2002b). Local polynomial fitting with long-memory, short-memory and antipersistent errors. Annals of the Institute of Statistical Mathematics, 54(2), 291-311.
Beran, J. and Feng, Y. (2002c). SEMIFAR models - a semiparametric approach to modelling trends, longrange dependence and nonstationarity. Computational Statistics & Data Analysis 40(2), 393-419.
Letmathe, S., Beran, J. and Feng, Y. (2021). An extended exponential SEMIFAR model with application in R. Discussion Paper. Paderborn University.