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tsDyn

Package tsDyn implements a variety of non-linear time series models. To read more about it, look at:

the wiki
the FAQ

Installation

You can install the released version of tsDyn from CRAN with:

install.packages("tsDyn")

Development version

Most of the development is hosted under the branch called Dev94. To install that version, use:

library(remotes)
remotes::install_github("MatthieuStigler/tsDyn/tsDyn", ref = "dev")

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Version

Install

install.packages('tsDyn')

Monthly Downloads

2,359

Version

11.0.4

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/MatthieuStigler/tsDyn/wiki

Maintainer

Matthieu Stigler

Last Published

January 26th, 2023

Functions in tsDyn (11.0.4)

Test of linearity

Specification of the threshold search

Mean Absolute Percent Error

US monthly industrial production from Hansen (1999)

Test of unit root against SETAR alternative

Simulation of a multivariate Threshold Autoregressive model (TVAR)

US unemployment series used in Caner and Hansen (2001)

No cointegration vs threshold cointegration test

Forecasting accuracy measures.

Test of linear cointegration vs threshold cointegration

Additive nonlinear autoregressive model

Estimation of Vector error correction model (VECM)

Virtual VECM model

Threshold Vector Error Correction model (VECM)

Simulation and bootstrap a VECM or bivariate TVECM

VAR representation

Simulate or bootstrap a VAR model

Long-term mean of an AR(p) process

Bivariate time series plots

Time series of PPI used as example in Bierens and Martins (2010)

autotriples.rgl

Interactive trivariate time series plots

Characteristic roots of the AR coefficients

availableModels

Available models

Impulse response function

Trivariate time series plots

delta test of linearity

Extract threshold(s) coefficient

delta test of conditional independence

Forecast Error Variance Decomposition

Linear AutoRegressive models

Multivariate linear models: VAR and VECM

fitted method for objects of class nlVar, i.e. VAR and VECM models.

Locally linear model

NLAR common structure

Non-linear time series model, base class definition

Extract Log-Likelihood

Logistic Smooth Transition AutoRegressive model

Neural Network nonlinear autoregressive model

Monthly US unemployment

Extract cointegration parameters A, B and PI

Selection of the lag with Information criterion.

computeGradient

computeGradient

Objects exported from other packages

Extract a variable showing the regime

Test of the cointegrating rank

Selection of the cointegrating rank with Information criterion.

Plot the Error Correct Term (ECT) response

predict_rolling

Rolling forecasts

Residual variance

Mean Square Error

Plotting methods for SETAR and LSTAR subclasses

Predict method for objects of class ‘VAR’, ‘VECM’ or ‘TVAR’

Self Threshold Autoregressive model

Automatic selection of SETAR hyper-parameters

Latex representation of fitted setar models

Resampling schemes

selectHyperParms

Automatic selection of model hyper-parameters

Predict method for objects of class ‘nlar’.

setarTest_IIPUs_results

Results from the setarTest, applied on Hansen (1999) data

sigmoid functions

Getting started with the tsDyn package

Simulation and bootstrap of Threshold Autoregressive model (SETAR)

Test of linearity against threshold (SETAR)

zeroyld time series

Test of unit root against SETAR alternative with

Generalized Impulse response Function (GIRF)

Multivariate Threshold Vector Autoregressive model