tsDyn: Nonlinear Time Series Models with Regime Switching

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).

Version: 10-1.2
Depends: R (≥ 3.5.0)
Imports: mnormt, mgcv, nnet, tseriesChaos, tseries, utils, vars, urca, forecast, MASS, Matrix, foreach, methods
Suggests: sm, scatterplot3d, rgl
Published: 2020-02-04
Author: Antonio Fabio Di Narzo [aut], Jose Luis Aznarte [ctb], Matthieu Stigler [aut], Ho Tsung-wu [cre]
Maintainer: Ho Tsung-wu <tsungwu at ntnu.edu.tw>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://github.com/MatthieuStigler/tsDyn/wiki
NeedsCompilation: yes
In views: Econometrics, Finance, TimeSeries
CRAN checks: tsDyn results


Reference manual: tsDyn.pdf
Package source: tsDyn_10-1.2.tar.gz
Windows binaries: r-devel: tsDyn_10-1.2.zip, r-release: tsDyn_10-1.2.zip, r-oldrel: tsDyn_10-1.2.zip
macOS binaries: r-release: tsDyn_10-1.2.tgz, r-oldrel: tsDyn_10-1.2.tgz
Old sources: tsDyn archive

Reverse dependencies:

Reverse depends: dvqcc
Reverse imports: GVARX, NonlinearTSA
Reverse suggests: mFilter, svars


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