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Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.


Unit root and cointegration tests encountered in applied econometric analysis are implemented.


Extraction of Factors from Multivariate Time Series. See ?00tsfa-Intro for more details.


Package for time series analysis and computational finance


Routines for the analysis of nonlinear time series. This work is largely inspired by the TISEAN project, by Rainer Hegger, Holger Kantz and Thomas Schreiber:


Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).


Functions for statistical analysis, prediction and control of time series.


The Direct Filter Approach (DFA) provides efficient estimates of signals at the current boundary of time series in real-time. For that purpose, one-sided ARMA-filters are computed by minimizing customized error criteria. The DFA can be used for estimating either the level or turning-points of a series, knowing that both criteria are incongruent. In the context of real-time turning- point detection, various risk-profiles can be operationalized, which account for the speed and/or the reliability of the one- sided filter.


Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY.


Provides a set of functions for performing digital signal processing.