## Class VARMAModel

• Direct Known Subclasses:
VARModel, VMAModel

public class VARMAModel
extends VARIMAModel
A multivariate ARMA model, Xt, takes this form. $X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,$
• ### Constructor Summary

Constructors
Constructor Description
VARMAModel​(Matrix[] phi, Matrix[] theta)
Construct a multivariate ARMA model with unit variance and zero-intercept (mu).
VARMAModel​(Matrix[] phi, Matrix[] theta, Matrix sigma)
Construct a multivariate ARMA model with zero-intercept (mu).
VARMAModel​(Vector mu, Matrix[] phi, Matrix[] theta)
Construct a multivariate ARMA model with unit variance.
VARMAModel​(Vector mu, Matrix[] phi, Matrix[] theta, Matrix sigma)
Construct a multivariate ARMA model.
VARMAModel​(VARMAModel that)
Copy constructor.
VARMAModel​(ARMAModel model)
Construct a multivariate model from a univariate ARMA model.
• ### Method Summary

All Methods
Modifier and Type Method Description
Vector conditionalMean​(Matrix arLags, Matrix maLags)
Compute the multivariate ARMA conditional mean, given all the lags.
VARMAModel getDemeanedModel()
Get the demeaned version of the time series model.
Vector unconditionalMean()
Compute the multivariate ARMA unconditional mean.
• ### Methods inherited from class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

getVARMA
• ### Methods inherited from class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

AR, d, dimension, getVARMAX, MA, maxPQ, mu, p, phi, psi, q, sigma, theta
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

• #### VARMAModel

public VARMAModel​(Vector mu,
Matrix[] phi,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARMA model.
Parameters:
mu - the intercept (constant) vector
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient
sigma - the white noise covariance matrix
• #### VARMAModel

public VARMAModel​(Vector mu,
Matrix[] phi,
Matrix[] theta)
Construct a multivariate ARMA model with unit variance.
Parameters:
mu - the intercept (constant) vector
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient
• #### VARMAModel

public VARMAModel​(Matrix[] phi,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARMA model with zero-intercept (mu).
Parameters:
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient
sigma - the white noise covariance matrix
• #### VARMAModel

public VARMAModel​(Matrix[] phi,
Matrix[] theta)
Construct a multivariate ARMA model with unit variance and zero-intercept (mu).
Parameters:
phi - the AR coefficients (excluding the initial 1); null if no AR coefficient
theta - the MA coefficients (excluding the initial 1); null if no MA coefficient
• #### VARMAModel

public VARMAModel​(ARMAModel model)
Construct a multivariate model from a univariate ARMA model.
Parameters:
model - a univariate ARMA model
• #### VARMAModel

public VARMAModel​(VARMAModel that)
Copy constructor.
Parameters:
that - a multivariate ARMA model
• ### Method Detail

• #### conditionalMean

public Vector conditionalMean​(Matrix arLags,
Matrix maLags)
Compute the multivariate ARMA conditional mean, given all the lags.
Parameters:
arLags - the AR lags
maLags - the MA lags
Returns:
the conditional mean
• #### unconditionalMean

public Vector unconditionalMean()
Compute the multivariate ARMA unconditional mean.
Returns:
the unconditional mean
• #### getDemeanedModel

public VARMAModel getDemeanedModel()
Get the demeaned version of the time series model. $Y_t = (X_t - \mu) = \sum_{i=1}^p \phi_i (X_{t-i} - \mu) + \sum_{i=1}^q \theta_j \epsilon_{t-j} + \epsilon_t$ μ is the unconditional mean.
Returns:
the demeaned time series