Class DLMSeries

  • All Implemented Interfaces:
    TimeSeries<Integer,​DLMSim.Innovation,​DLMSeries.Entry>, Iterable<DLMSeries.Entry>

    public class DLMSeries
    extends Object
    implements TimeSeries<Integer,​DLMSim.Innovation,​DLMSeries.Entry>
    This is a simulator for a multivariate controlled dynamic linear model process. For (t ≥ 1), a controlled DLM takes the following form:

    Observation Equation:

    yt = Ft * xt + vt,
    State Equation:
    xt = Gt * xt-1 + Ht * ut + wt,
    Given the model parameters, the time series of control variables {ut} and an integer (length) T, This simulator generates both the states {xt} and observations {yt}.

    This implementation is appropriate for a short time series when the size is known; otherwise use instead DLMSim.

    • Constructor Detail

      • DLMSeries

        public DLMSeries​(int T,
                         DLM model,
                         double[] u,
                         RandomStandardNormalGenerator rnorm)
        Simulate a univariate controlled dynamic linear model process.
        Parameters:
        T - the length of the univariate time series (states and observations) to generate
        model - a univariate controlled dynamic linear model
        u - a time series of control variables (length = T)
        rnorm - a standard Gaussian random number generator (for seeding)
      • DLMSeries

        public DLMSeries​(int T,
                         DLM model,
                         double[] u)
        Simulate a univariate controlled dynamic linear model process.
        Parameters:
        T - the length of the univariate time series (states and observations) to generate
        model - a univariate controlled dynamic linear model
        u - a time series of control variables (length = T)
      • DLMSeries

        public DLMSeries​(int T,
                         DLM model)
        Simulate a univariate controlled dynamic linear model process.
        Parameters:
        T - the length of the univariate time series (states and observations) to generate
        model - a univariate controlled dynamic linear model