tsDyn

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).

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References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
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  1. De Luca, Giovanni; Zuccolotto, Paola: Regime dependent interconnectedness among fuzzy clusters of financial time series (2021)
  2. Kuschnig, N., Vashold, L.: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R (2021) not zbMATH
  3. Alexander Lange, Bernhard Dalheimer, Helmut Herwartz, Simone Maxand: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis (2020) not zbMATH
  4. Vinod, Hrishikesh D. (ed.); Rao, C. R. (ed.): Financial, macro and micro econometrics using R (2020)
  5. Wegener, Michael; Kauermann, Göran: Forecasting in nonlinear univariate time series using penalized splines (2017)
  6. dos Santos, Marcelo Justus; Kassouf, Ana Lúcia: A cointegration analysis of crime, economic activity, and police performance in São Paulo city (2013)
  7. Aznarte, José Luis; Alcalá-Fdez, Jesús; Arauzo, Antonio; Benítez, José Manuel: Fuzzy autoregressive rules: towards linguistic time series modeling (2011)
  8. Aznarte, José Luis; Medeiros, Marcelo C.; Benítez, José M.: Linearity testing for fuzzy rule-based models (2010)
  9. Kleiber, Christian; Zeileis, Achim: Applied econometrics with R (2008)