| Class | Description |
|---|---|
| VARFit |
This class construct a VAR model by estimating the coefficients using OLS regression.
|
| VARLinearRepresentation |
The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated)
infinite sum of AR terms.
|
| VARMAAutoCorrelation |
Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
|
| VARMAAutoCovariance |
Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
EXt = 0.
|
| VARMAForecastOneStep |
This is an implementation, adapted for an ARMA process, of the innovation algorithm,
which is an efficient way of obtaining a one step least square linear predictor.
|
| VARMAModel |
A multivariate ARMA model, Xt, takes this form.
|
| VARMAXModel |
The VARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by
incorporating exogenous variables.
|
| VARModel |
This class represents a VAR model.
|
| VARXModel |
A VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes
this form.
|
| VECM |
A Vector Error Correction Model (VECM(p)) has one of the following specifications:
Transitory:
\[
\Delta Y_t = \mu + \Pi Y_{t-1} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1
\]
or
Long-run:
\[
\Delta Y_t = \mu + \Pi Y_{t-p} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1
\]
Yt, μ and εt are n-dimensional vectors.
|
| VECMLongrun |
The long-run Vector Error Correction Model (VECM(p)) takes this form.
|
| VECMTransitory |
A transitory Vector Error Correction Model (VECM(p)) takes this form.
|
| VMAInvertibility |
The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.
|
| VMAModel |
This class represents a multivariate MA model.
|
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