Uses of Package
dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

Packages that use dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

Package	Description
dev.nm.stat.timeseries.linear.multivariate.arima
dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
tech.nmfin.portfoliooptimization.lai2010.fit

Classes in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma used by dev.nm.stat.timeseries.linear.multivariate.arima

Class and Description
VARMAModel A multivariate ARMA model, \(X_{t}\), takes this form. \[ X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]
VARMAXModel The VARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.

Classes in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma used by dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

Class and Description
VARMAModel A multivariate ARMA model, \(X_{t}\), takes this form. \[ X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]
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, \(X_{t}\), takes this form. \[ Y_t = \mu + \Sigma \phi_i * Y_{t-i} + \Psi * D_t + \epsilon_t \] where \(Y_{t}\), μ and \(\epsilon _{t}\) are n-dimensional vectors.
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: \[ \Delta Y_t = \mu + \Pi Y_{t-p} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1 \] \(Y_{t}\), μ and \(\epsilon _{t}\) are n-dimensional vectors.
VECMLongrun The long-run Vector Error Correction Model (VECM(p)) takes this form. \[ \Delta Y_t = \mu + \Pi Y_{t-p} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1 \] \(Y_{t}\), μ and \(\epsilon _{t}\) are n-dimensional vectors.
VECMTransitory A transitory Vector Error Correction Model (VECM(p)) takes this form. \[ \Delta Y_t = \mu + \Pi Y_{t-1} + \Sigma \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1 \] \(Y_{t}\), μ and \(\epsilon _{t}\) are n-dimensional vectors.
VMAModel This class represents a multivariate MA model.

Classes in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma used by tech.nmfin.portfoliooptimization.lai2010.fit

Class and Description
VARMAModel A multivariate ARMA model, \(X_{t}\), takes this form. \[ X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]
VARModel This class represents a VAR model.