Class DLM

  • Direct Known Subclasses:
    Elliott2005DLM

    public class DLM
    extends Object
    This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification. A controlled DLM, for (t ≥ 1), is described by two equations: the observation and state equations.

    Observation Equation:

    yt = Ft * xt + vt,
    State Equation:
    xt = Gt * xt-1 + Ht * ut + wt,
    {yt} are the observation vectors; {xt} are the state vectors. Ft and Gt are known matrices of dimension (number of observations * number of states) and (number of states * number of states) respectively. {vt} and {wt} are two independent sequences of independent normal random vectors with mean zero and known variance matrices {Vt} and {Wt}, respectively; Furthermore, it is assumed that x0 is independent of {vt} and {wt} and is normally distributed with mean m0 and covariance matrix C0, where m0 is a vector of length the same as the number of states and C0 is a matrix of dimension (number of states * number of states); ut is an m-dimensional vector of control variables, i.e., the variables whose values can be regulated by the user, in order to obtain a desired level of the state xt. Ht is a known matrix of coefficients, with dimension of (number of states * m).
    See Also:
    • Constructor Detail

      • DLM

        public DLM​(double m0,
                   double C0,
                   ObservationEquation Yt,
                   StateEquation Xt)
        Construct a univariate controlled dynamic linear model.
        Parameters:
        m0 - the mean of x0
        C0 - the variance of x0
        Yt - the observation equation for the model
        Xt - the state equation for the model
      • DLM

        public DLM​(DLM that)
        Copy constructor.
        Parameters:
        that - a univariate controlled dynamic linear model
    • Method Detail

      • m0

        public double m0()
        Get the the mean of x0.
        Returns:
        m0, the mean of x0
      • C0

        public double C0()
        Get the variance of x0.
        Returns:
        C0, the variance of x0
      • getObsDimension

        public int getObsDimension()
        Get the dimension of observations.
        Returns:
        the dimension of observations
      • getStateDimension

        public int getStateDimension()
        Get the dimension of states.
        Returns:
        the dimension of states
      • getObservationModel

        public ObservationEquation getObservationModel()
        Get the observation model.
        Returns:
        the observation model
      • getStateModel

        public StateEquation getStateModel()
        Get the state model.
        Returns:
        the state model