smoots swMATH ID: 35248 Software Authors: Yuanhua Feng, Dominik Schulz, Thomas Gries, Marlon Fritz, Sebastian Letmathe Description: R package 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. The methods of the package are described in Feng, Y., and Gries, T., (2017) <http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP102.pdf>. A current version of the paper that is also referred to in the documentation of the functions is prepared for publication. Homepage: https://cran.r-project.org/web/packages/smoots/index.html Dependencies: R Keywords: bandwidth selection; dependent errors; derivative estimation; spectral density; semiparametric modelling; implementation in R Related Software: R Cited in: 1 Publication Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year Data-driven local polynomial for the trend and its derivatives in economic time series. Zbl 1444.62104Feng, Yuanhua; Gries, Thomas; Fritz, Marlon 2020 Cited by 3 Authors 1 Feng, Yuanhua 1 Fritz, Marlon 1 Gries, Thomas Cited in 1 Serial 1 Journal of Nonparametric Statistics Cited in 2 Fields 1 Statistics (62-XX) 1 Game theory, economics, finance, and other social and behavioral sciences (91-XX) Citations by Year