A time series is said to be partially autoregressive if it can be represented as a sum of a random walk and an autoregressive sequence without unit roots. This package fits partially autoregressive time series, where the autoregressive  component is AR(1).  This may be of use in modeling certain financial time series.
Rcpp (>= 0.11.2), zoo, parallel, ggplot2, MASS, tseries,
data.table, FKF, urca, plot3D, methods

Rcpp (>= 0.11.2), zoo, parallel, ggplot2, MASS, tseries,
data.table, KFAS, urca, plot3D, methods

Fits time series models which consist of a sum of a permanent and a transient component.
Rcpp (>= 0.11.2), zoo, parallel, ggplot2, grid, MASS, tseries,
data.table, FKF, urca, plot3D

c(person("Matthew", "Clegg", role=c("aut"),
email="matthewcleggphd@gmail.com"),
person("Christopher", "Krauss", role=c("aut"),
email="christopher.krauss@fau.de"),
person("Jonas", "Rende", role=c("cre","aut"),
email="jonas.rende@fau.de"))
Matthew Clegg [aut],
Christopher Krauss [aut],
Jonas Rende [cre, aut]
A collection of time series is partially cointegrated if a linear combination of these time series can be found so that the residual spread is partially autoregressive - meaning that it can be represented as a sum of an autoregressive series and a random walk. This concept is useful in modeling certain sets of financial time series and beyond, as it allows for the spread to contain transient and permanent components alike. Partial cointegration has been introduced by Clegg and Krauss (2016) <https://hdl.handle.net/10419/140632>, along with a large-scale empirical application to financial market data. The partialCI package comprises estimation, testing, and simulation routines for partial cointegration models in state space. Clegg et al. (2017) <https://hdl.handle.net/10419/150014> provide an in in-depth discussion of the package functionality as well as illustrating examples in the fields of finance and macroeconomics.
zoo, parallel, ggplot2, grid, MASS, TTR, data.table, glmnet,
methods, Rcpp, FKF