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Title: The smoots Package in R for semiparametric modeling of trend stationary time series Authors:  Yuanhua Feng - Paderborn University (Germany)
Thomas Gries - Paderborn University (Germany)
Sebastian Letmathe - Paderborn University (Germany)
Dominik Schulz - Paderborn University (Germany) [presenting]
Abstract: An introduction is given to the new package in R called "smoots" (smoothing time series), developed for data-driven local polynomial smoothing of trend-stationary time series. Functions for data-driven estimation of the first and second derivatives of the trend are also built-in. It is first applied to monthly changes in the global temperature. The quarterly US-GDP series shows that this package can also be well applied to a semiparametric multiplicative component model for non-negative time series via the log-transformation. Furthermore, we introduced a semiparametric Log-GARCH and a semiparametric Log-ACD model, which can be easily estimated by the "smoots" package. Of course, this package applies to suitable time series from any other research area. The smoots package also provides a useful tool for teaching time series analysis, because many practical time series follow an additive or a multiplicative component model.