Tools for the Analysis of Weak ARMA Models
Computation of autocovariance and autocorrelation for an ARMA residual...
Computation of autocovariance and autocorrelation for an ARMA residual...
Selection of ARMA models
Parameters estimation of a time series.
Computation the gradient of the residuals of an ARMA model
Estimation of Fisher information matrix I
Function optim will minimize
Autocorrelogram
Computation of Fisher information matrice
Portmanteau tests for one lag.
Portmanteau tests
Computes the parameters significance
Simulation of ARMA(p,q) model.
GARCH process
Estimation of VAR(p) model
Weak white noise
Weak white noise
Weak white noise
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.
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