weakARMA: Tools for the Analysis of Weak ARMA Models

Numerous time series admit autoregressive moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent. These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments 'p', 'q', 'ar' and 'ma'. See Francq, C. and Zakoïan, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and Boubacar Maïnassara, Y. and Saussereau, B. (2018) <doi:10.1080/01621459.2017.1380030> for more details.

Getting started

Package details

AuthorYacouba Boubacar Maïnassara [aut] (<https://orcid.org/0000-0002-8604-5407>), Julien Yves Rolland [aut, cre] (<https://orcid.org/0000-0002-0960-6688>), Coraline Parguey [ctb], Vincent Mouillot [ctb]
MaintainerJulien Yves Rolland <julien.rolland@univ-fcomte.fr>
LicenseGPL (>= 3)
URL https://plmlab.math.cnrs.fr/jrolland/weakARMA
Package repositoryView on CRAN
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weakARMA documentation built on April 5, 2022, 1:16 a.m.