|
Class Summary |
| VARFitting |
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. |
| VARMAInnovationAlgorithm |
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 ARMAX 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. |