public class MultivariateStateEquation extends Object
xt = Gt * xt-1 + Ht * ut + wt,
Constructor and Description |
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MultivariateStateEquation(Matrix G,
Matrix W)
Constructs a time-invariant state equation without control variables.
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MultivariateStateEquation(Matrix G,
Matrix H,
Matrix W,
NormalRVG rmvnorm)
Constructs a time-invariant state equation.
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MultivariateStateEquation(MultivariateStateEquation that)
Copy constructor.
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MultivariateStateEquation(R1toMatrix G,
R1toMatrix W)
Constructs a state equation without control variables.
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MultivariateStateEquation(R1toMatrix G,
R1toMatrix H,
R1toMatrix W)
Constructs a state equation.
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MultivariateStateEquation(R1toMatrix G,
R1toMatrix H,
R1toMatrix W,
NormalRVG rmvnorm)
Constructs a state equation.
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MultivariateStateEquation(StateEquation states)
Constructs a multivariate state equation from a univariate state equation.
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Modifier and Type | Method and Description |
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int |
dimension()
Gets the dimension of state xt.
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Matrix |
G(int t)
Gets G(t), the coefficient matrix of xt - 1.
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Matrix |
H(int t)
Gets H(t), the covariance matrix of ut.
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Matrix |
W(int t)
Gets W(t), the covariance matrix of wt.
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Vector |
xt_mean(int t,
Vector xt_1)
Predicts the next state without control variable.
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Vector |
xt_mean(int t,
Vector xt_1,
Vector ut)
Predicts the next state.
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ImmutableMatrix |
xt_var(int t,
Matrix var_tlag_tlag)
Gets the variance of the apriori prediction for the next state.
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ImmutableVector |
xt(int t,
Vector xt_1)
Evaluates the state equation without the control variable.
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ImmutableVector |
xt(int t,
Vector xt_1,
Vector ut)
Evaluates the state equation.
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public MultivariateStateEquation(R1toMatrix G, R1toMatrix H, R1toMatrix W, NormalRVG rmvnorm)
G
- the coefficient matrix function of xt - 1H
- the coefficient matrix function of control variables ut; use
null
if there isn't anyW
- the covariance matrix function of wtrmvnorm
- a p-dimensional standard multivariate Gaussian random vector generator
(for seeding); p = the dimension of W or xtpublic MultivariateStateEquation(R1toMatrix G, R1toMatrix H, R1toMatrix W)
G
- the coefficient matrix function of xt - 1H
- the coefficient matrix function of control variables ut; use
null
if there isn't anyW
- the covariance matrix function of wtpublic MultivariateStateEquation(R1toMatrix G, R1toMatrix W)
G
- the coefficient matrix function of xt - 1W
- the covariance matrix function of wtpublic MultivariateStateEquation(Matrix G, Matrix H, Matrix W, NormalRVG rmvnorm)
G
- the coefficient matrix function of xt - 1H
- the coefficient matrix function of control variables ut; use
null
if there isn't anyW
- the covariance matrix function of wtrmvnorm
- a p-dimensional standard multivariate Gaussian random vector generator;
p = the dimension of W or xtpublic MultivariateStateEquation(Matrix G, Matrix W)
G
- the coefficient matrix function of xt - 1W
- the covariance matrix function of wtpublic MultivariateStateEquation(StateEquation states)
states
- a univariate state equationpublic MultivariateStateEquation(MultivariateStateEquation that)
that
- a StateEquation
public int dimension()
public Matrix G(int t)
t
- timepublic Matrix H(int t)
t
- timepublic Matrix W(int t)
t
- timepublic Vector xt_mean(int t, Vector xt_1, Vector ut)
E(x_t) = G_t * x_{t - 1} + H_t * u_t
t
- timext_1
- x lag xt - 1ut
- the control variable utpublic Vector xt_mean(int t, Vector xt_1)
E(x_t) = G_t * x_{t - 1} + H_t * u_t
t
- timext_1
- x lag xt - 1public ImmutableMatrix xt_var(int t, Matrix var_tlag_tlag)
Var(x_{t | t - 1}) = G_t * Var(x_{t - 1| t - 1}) * G_t' + W_t
t
- timevar_tlag_tlag
- Var(x_{t - 1 | t - 1}), the covariance of the posterior updatepublic ImmutableVector xt(int t, Vector xt_1, Vector ut)
x_t = G_t * x_{t - 1} + H_t * u_t + w_t
t
- timext_1
- x lag xt - 1ut
- the control variable utpublic ImmutableVector xt(int t, Vector xt_1)
x_t = G_t * x_{t - 1} + H_t * u_t + w_t
t
- timext_1
- x lag xt - 1Copyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.