Package dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

Class VARMAAutoCorrelation

java.lang.Object

dev.nm.analysis.function.matrix.R2toMatrix

dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction

dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation

All Implemented Interfaces:

Function<Vector,Matrix>, RntoMatrix

public class VARMAAutoCorrelation
extends MultivariateAutoCorrelationFunction

Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.

This implementation solves the Yule-Walker equation.

The R equivalent functions are ARMAacf and TacvfAR in package FitAR.

Nested Class Summary

Nested classes/interfaces inherited from interface dev.nm.analysis.function.Function

Function.EvaluationException

Constructor Summary

Constructors

Constructor	Description
VARMAAutoCorrelation(VARMAModel model, int nLags)	Compute the auto-correlation function for a vector ARMA model.

Method Summary

Modifier and Type	Method	Description
Matrix	evaluate(double i)	Get the i-th auto-correlation matrix.
Matrix	evaluate(double i, double j)	Evaluate f(x1, x2) = A.

Methods inherited from class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction

get

Methods inherited from class dev.nm.analysis.function.matrix.R2toMatrix

dimensionOfDomain, dimensionOfRange, evaluate

Methods inherited from class java.lang.Object

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

Constructor Detail

VARMAAutoCorrelation

public VARMAAutoCorrelation(VARMAModel model,
                            int nLags)

Compute the auto-correlation function for a vector ARMA model.

Parameters:

model - an ARIMA model

nLags - the number of lags

Method Detail

evaluate

public Matrix evaluate(double i,
                       double j)

Description copied from class: R2toMatrix

Evaluate f(x₁, x₂) = A.

Specified by:

evaluate in class R2toMatrix

Parameters:

i - x1

j - x2

Returns:

f(x1, x2)

evaluate

public Matrix evaluate(double i)

Get the i-th auto-correlation matrix.

Parameters:

i - the lag order

Returns:

the i-th auto-correlation matrix