Although linear autoregressive models are useful to practitioners in different fields, often a nonlinear specification would be more appropriate in time series analysis. In general, there are many alternative approaches to nonlinearity modelling, one consists in assuming multiple regimes. Among the possible specifications that account for regime changes in the multivariate framework, smooth transition models are the most general, since they nest both linear and threshold autoregressive models. This paper introduces the starvars package which estimates and predicts the Vector Logistic Smooth Transition model in a very general setting which also includes predetermined variables. In comparison to the existing R packages, starvars offers the estimation of the Vector Smooth Transition model both by maximum likelihood and nonlinear least squares. The package allows also to test for nonlinearity in a multivariate setting and detect the presence of common breaks. Furthermore, the package computes multi-step-ahead forecasts. Finally, an illustration with financial time series is provided to show its usage.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-018.zip
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For attribution, please cite this work as
Bucci, et al., "The R Journal: starvars: An R Package for Analysing Nonlinearities in Multivariate Time Series", The R Journal, 2022
BibTeX citation
@article{RJ-2022-018,
author = {Bucci, Andrea and Palomba, Giulio and Rossi, Eduardo},
title = {The R Journal: starvars: An R Package for Analysing Nonlinearities in Multivariate Time Series},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-018},
doi = {10.32614/RJ-2022-018},
volume = {14},
issue = {1},
issn = {2073-4859},
pages = {208-226}
}