smoots: Nonparametric Estimation of the Trend and Its Derivatives in TS

The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary 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. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.

Getting started

Package details

AuthorYuanhua Feng [aut] (Paderborn University, Germany), Sebastian Letmathe [aut] (Paderborn University, Germany), Dominik Schulz [aut, cre] (Paderborn University, Germany), Thomas Gries [ctb] (Paderborn University, Germany), Marlon Fritz [ctb] (Paderborn University, Germany)
MaintainerDominik Schulz <schulzd@mail.uni-paderborn.de>
LicenseGPL-3
Version1.1.0
URL https://wiwi.uni-paderborn.de/en/dep4/feng/ https://wiwi.uni-paderborn.de/dep4/gries/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("smoots")

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smoots documentation built on May 12, 2021, 1:06 a.m.