Learn R Programming

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")

Copy Link

Version

Install

install.packages('tsDyn')

Monthly Downloads

3,087

Version

11.0.4.1

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

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

Maintainer

Matthieu Stigler

Last Published

February 1st, 2024

Functions in tsDyn (11.0.4.1)

Test of linearity

Additive nonlinear autoregressive model

Forecasting accuracy measures.

No cointegration vs threshold cointegration test

Simulation and bootstrap a VECM or bivariate TVECM

Threshold Vector Error Correction model (VECM)

Estimation of Vector error correction model (VECM)

Test of linear cointegration vs threshold cointegration

Long-term mean of an AR(p) process

autotriples.rgl

Interactive trivariate time series plots

availableModels

Available models

Bivariate time series plots

Trivariate time series plots

Virtual VECM model

Characteristic roots of the AR coefficients

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

Forecast Error Variance Decomposition

delta test of conditional independence

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

delta test of linearity

Mean Square Error

Simulate or bootstrap a VAR model

Extract threshold(s) coefficient

Impulse response function

Multivariate linear models: VAR and VECM

Linear AutoRegressive models

Residual variance

Extract a variable showing the regime

VAR representation

Neural Network nonlinear autoregressive model

Extract cointegration parameters A, B and PI

computeGradient

computeGradient

Selection of the lag with Information criterion.

Non-linear time series model, base class definition

Locally linear model

Plotting methods for SETAR and LSTAR subclasses

Extract Log-Likelihood

Resampling schemes

Plot the Error Correct Term (ECT) response

Objects exported from other packages

sigmoid functions

Test of the cointegrating rank

selectHyperParms

Automatic selection of model hyper-parameters

setarTest_IIPUs_results

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

Latex representation of fitted setar models

NLAR common structure

Test of linearity against threshold (SETAR)

Simulation and bootstrap of Threshold Autoregressive model (SETAR)

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

Monthly US unemployment

Predict method for objects of class ‘nlar’.

Logistic Smooth Transition AutoRegressive model

Getting started with the tsDyn package

zeroyld time series

Automatic selection of SETAR hyper-parameters

predict_rolling

Rolling forecasts

Self Threshold Autoregressive model

Selection of the cointegrating rank with Information criterion.

Generalized Impulse response Function (GIRF)

Test of unit root against SETAR alternative

Mean Absolute Percent Error

Specification of the threshold search

Test of unit root against SETAR alternative with

Multivariate Threshold Vector Autoregressive model

Simulation of a multivariate Threshold Autoregressive model (TVAR)

US monthly industrial production from Hansen (1999)

US unemployment series used in Caner and Hansen (2001)