Direct Known Subclasses:
VARModel, VMAModel

public class VARMAModel extends VARIMAModel
A multivariate ARMA model, Xt, takes this form. \[ X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]
See Also:
  • Constructor Details

    • 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 Details

    • 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