Package: tseriesTARMA 0.5-1

Simone Giannerini

tseriesTARMA: Analysis of Nonlinear Time Series Through Threshold Autoregressive Moving Average Models (TARMA) Models

Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) <doi:10.1080/07350015.2024.2412011>; tests for threshold effects, see Giannerini et al. JoE (2024) <doi:10.1016/j.jeconom.2023.01.004>, Goracci et al. Statistica Sinica (2023) <doi:10.5705/ss.202021.0120>, Angelini et al. (2024) <doi:10.48550/arXiv.2308.00444>; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) <doi:10.5705/ss.202022.0125>.

Authors:Simone Giannerini [aut, cre], Greta Goracci [aut]

tseriesTARMA_0.5-1.tar.gz
tseriesTARMA_0.5-1.tar.gz(r-4.5-noble)tseriesTARMA_0.5-1.tar.gz(r-4.4-noble)
tseriesTARMA_0.5-1.tgz(r-4.4-emscripten)
tseriesTARMA.pdf |tseriesTARMA.html✨
tseriesTARMA/json (API)
NEWS

# Install 'tseriesTARMA' in R:
install.packages('tseriesTARMA', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • ACValues - Andrews Tabulated Critical Values
  • supLMQur - Tabulated Critical Values for the Unit Root IMA vs TARMA test

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortranopenblas

3.06 score 23k downloads 10 exports 35 dependencies

Last updated 6 months agofrom:f2299190b4. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 08 2025
R-4.5-linux-x86_64OKMar 08 2025
R-4.4-linux-x86_64OKMar 08 2025

Exports:plot.tsfitTAR.testTAR.test.BTARMA.fitTARMA.fit2TARMA.simTARMA.testTARMAGARCH.testTARMAur.testTARMAur.test.B

Dependencies:chronDistributionUtilsfitdistrplusFNNfracdiffGeneralizedHyperbolickernlabKernSmoothkslatticelbfgsb3cMASSmathjaxrMatrixmclustmgcvmulticoolmvtnormnlmenloptrnumDerivpracmarbibutilsRcppRcppArmadilloRdpackrlangRsolnprugarchSkewHyperbolicspdsurvivaltruncnormxtszoo

Citation

To cite package ‘tseriesTARMA’ in publications use:

Giannerini S, Goracci G (2024). tseriesTARMA: Analysis of Nonlinear Time Series Through Threshold Autoregressive Moving Average Models (TARMA) Models. R package version 0.5-1, https://CRAN.R-project.org/package=tseriesTARMA.

Corresponding BibTeX entry: 

  @Manual{,
    title = {tseriesTARMA: Analysis of Nonlinear Time Series Through
      Threshold Autoregressive Moving Average Models (TARMA) Models},
    author = {Simone Giannerini and Greta Goracci},
    year = {2024},
    note = {R package version 0.5-1},
    url = {https://CRAN.R-project.org/package=tseriesTARMA},
  }

Readme and manuals

tseriesTARMA

Analysis of Nonlinear Time Series through Threshold Autoregressive Moving Average Models (TARMA) models

It provides advanced functions for:

  • TARMA model fitting and forecasting:
    • Least Squares fitting of a full subset TARMA model, including robust fitting based on M-estimators.
    • Maximum Likelihood fitting of a subset TARMA model with common MA parts and possible covariates.
  • TARMA testing for threshold type nonlinearity:
    • Tests for AR vs TAR (asymptotic, bootstrap, wild bootstrap)
    • Tests for ARMA vs TARMA with both i.i.d. errors and GARCH errors.
  • Unit-root testing against a stationary TARMA model

Installation

install.packages("tseriesTARMA")

Authors

References

  • Goracci et al. (2025)
  • Angelini et al. (2023)
  • Giannerini, Goracci, and Rahbek (2024)
  • Goracci, Ferrari, et al. (2023)
  • Giannerini, Goracci, and Rahbek (2022)
  • Giannerini and Goracci (2021)
  • Goracci et al. (2021)
  • Goracci, Giannerini, et al. (2023)
  • K.-S. Chan and Goracci (2019)
  • K.-S. Chan et al. (2024)

Angelini, F., M. Castellani, S. Giannerini, and G. Goracci. 2023. “Testing for Threshold Effects in Presence of Heteroskedasticity and Measurement Error with an Application to Italian Strikes.” University of Bologna; Free University of Bolzano. https://arxiv.org/abs/2308.00444.

Chan, K. -S., and G. Goracci. 2019. “On the Ergodicity of First-Order Threshold Autoregressive Moving-Average Processes.” J. Time Series Anal. 40 (2): 256–64.

Chan, K.-S., S. Giannerini, G. Goracci, and H. Tong. 2024. “Testing for Threshold Regulation in Presence of Measurement Error.” Statistica Sinica 34 (3): 1413–34. https://doi.org/10.5705/ss.202022.0125.

Giannerini, S., and G. Goracci. 2021. “Estimating and Forecasting with TARMA Models.” University of Bologna.

Giannerini, S., G. Goracci, and A. Rahbek. 2022. “The Validity of Bootstrap Testing in the Threshold Framework.” arXiv. https://doi.org/10.48550/ARXIV.2201.00028.

———. 2024. “The Validity of Bootstrap Testing in the Threshold Framework.” Journal of Econometrics 239 (1): 105379. https://doi.org/10.1016/j.jeconom.2023.01.004.

Goracci, G., D. Ferrari, S. Giannerini, and F. Ravazzolo. 2023. “Robust Estimation for Threshold Autoregressive Moving-Average Models.” Free University of Bolzano, University of Bologna. https://doi.org/10.48550/ARXIV.2211.08205.

———. 2025. “Robust Estimation for Threshold Autoregressive Moving-Average Models.” Journal of Business and Economic Statistics. (.): in press. https://doi.org/10.1080/07350015.2024.2412011.

Goracci, G., S. Giannerini, K.-S. Chan, and H. Tong. 2021. “Testing for Threshold Effects in the TARMA Framework.” University of Bologna, Free University of Bolzano, University of Iowa, London School of Economics. https://arxiv.org/abs/2103.13977.

———. 2023. “Testing for Threshold Effects in the TARMA Framework.” Statistica Sinica 33 (3): 1879–1901. https://doi.org/https://doi.org/10.5705/ss.202021.0120.

Help Manual

Help pageTopics
Andrews Tabulated Critical ValuesACValues
Plot from fitted/forecasted time series models.plot.tsfit
Forecast from fitted TARMA models.predict.TARMA
Methods for TARMA fitscoef.TARMA print.TARMA residuals.TARMA vcov.TARMA
Methods for TARMA testsprint.TARMAtest
Tabulated Critical Values for the Unit Root IMA vs TARMA testsupLMQur
AR versus TARMA supLM robust test for nonlinearityTAR.test
AR versus TAR bootstrap supLM test for nonlinearityTAR.test.B
TARMA Modelling of Time SeriesTARMA.fit
TARMA Modelling of Time SeriesTARMA.fit2
Simulation of a two-regime 'TARMA(p1,p2,q1,q2)' processTARMA.sim
ARMA versus TARMA (and AR versus TAR) supLM tests for nonlinearityTARMA.test
ARMA GARCH versus TARMA GARCH supLM test for nonlinearityTARMAGARCH.test
Unit root supLM test for an integrated MA versus a stationary TARMA processTARMAur.test
Unit root supLM test for an integrated MA versus a stationary TARMA processTARMAur.test.B