com.numericalmethod.suanshu.stats.dlm.univariate

Class StateEquation

java.lang.Object

com.numericalmethod.suanshu.stats.dlm.univariate.StateEquation

All Implemented Interfaces:

java.io.Serializable

public class StateEquation
extends java.lang.Object
implements java.io.Serializable

This is the state equation in a controlled dynamic linear model.

x_t = G_t * x_t-1 + H_t * u_t + w_t,

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
StateEquation(double G, double W) Construct a time-invariant state equation without control variables.
StateEquation(double G, double H, double W, NormalRng rnorm) Construct a time-invariant state equation.
StateEquation(StateEquation that) Copy constructor.
StateEquation(UnivariateRealFunction G, UnivariateRealFunction W) Construct a state equation without control variables.
StateEquation(UnivariateRealFunction G, UnivariateRealFunction H, UnivariateRealFunction W) Construct a state equation.
StateEquation(UnivariateRealFunction G, UnivariateRealFunction H, UnivariateRealFunction W, NormalRng rnorm) Construct a state equation.

Method Summary

Modifier and Type	Method and Description
int	dimension() Get the dimension of state xt.
double	G(int t) Get G(t), the coefficient of xt - 1.
double	H(int t) Get H(t), the variance of ut.
double	W(int t) Get W(t), the variance of wt.
double	xt_mean(int t, double xt_1) Predict the next state without control variable.
double	xt_mean(int t, double xt_1, double ut) Predict the next state.
double	xt_var(int t, double var_tlag_tlag) Get the variance of the apriori prediction for the next state.
double	xt(int t, double xt_1) Evaluate the state equation without the control variable.
double	xt(int t, double xt_1, double ut) Evaluate the state equation.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

StateEquation

public StateEquation(UnivariateRealFunction G,
                     UnivariateRealFunction H,
                     UnivariateRealFunction W,
                     NormalRng rnorm)

Construct a state equation.

Parameters:

G - the coefficient function of x_{t - 1}

H - the coefficient function of control variables u_t

W - the variance function of w_t

rnorm - a standard Gaussian random number generator (for seeding)

StateEquation

public StateEquation(UnivariateRealFunction G,
                     UnivariateRealFunction H,
                     UnivariateRealFunction W)

Construct a state equation.

Parameters:

G - the coefficient function of x_{t - 1}

H - the coefficient function of control variables u_t

W - the variance function of w_t

StateEquation

public StateEquation(UnivariateRealFunction G,
                     UnivariateRealFunction W)

Construct a state equation without control variables.

Parameters:

G - the coefficient function of x_{t - 1}

W - the variance function of w_t

StateEquation

public StateEquation(double G,
                     double H,
                     double W,
                     NormalRng rnorm)

Construct a time-invariant state equation.

Parameters:

G - the coefficient of x_{t - 1}

H - the coefficient of control variables u_t

W - the variance of w_t

rnorm - a standard Gaussian random number generator

StateEquation

public StateEquation(double G,
                     double W)

Construct a time-invariant state equation without control variables.

Parameters:

G - the coefficient of x_{t - 1}

W - the variance of w_t

StateEquation

public StateEquation(StateEquation that)

Copy constructor.

Parameters:

that - a StateEquation

Method Detail

dimension

public int dimension()

Get the dimension of state x_t.

Returns:

the dimension of states

G

public double G(int t)

Get G(t), the coefficient of x_{t - 1}.

Parameters:

t - time

Returns:

G(t)

H

public double H(int t)

Get H(t), the variance of u_t.

Parameters:

t - time

Returns:

H(t)

W

public double W(int t)

Get W(t), the variance of w_t.

Parameters:

t - time

Returns:

W(t)

xt_mean

public double xt_mean(int t,
                      double xt_1,
                      double ut)

Predict the next state.

E(x_t) = G_t * x_{t - 1} + H_t * u_t

Parameters:

t - time

xt_1 - x lag x_{t - 1}

ut - the control variable u_t

Returns:

x_t

xt_mean

public double xt_mean(int t,
                      double xt_1)

Predict the next state without control variable.

E(x_t) = G_t * x_{t - 1} + H_t * u_t

Parameters:

t - time

xt_1 - x lag x_{t - 1}

Returns:

x_t

xt_var

public double xt_var(int t,
                     double var_tlag_tlag)

Get the variance of the apriori prediction for the next state.

Var(x_{t | t - 1}) = G_t * Var(x_{t - 1| t - 1}) * G_t' + W_t

Parameters:

t - time

var_tlag_tlag - Var(x_{t - 1 | t - 1}), the variance of the posterior update

Returns:

Var(x_{t | t - 1})

xt

public double xt(int t,
                 double xt_1,
                 double ut)

Evaluate the state equation.

x_t = G_t * x_{t - 1} + H_t * u_t + w_t

Parameters:

t - time

xt_1 - x lag x_{t - 1}

ut - the control variable u_t

Returns:

x_t

xt

public double xt(int t,
                 double xt_1)

Evaluate the state equation without the control variable.

x_t = G_t * x_{t - 1} + H_t * u_t + w_t

Parameters:

t - time

xt_1 - x lag x_{t - 1}

Returns:

x_t