FitAR (version 1.94)

SimulateGaussianAR: Autoregression Simulation

Description

Simulate a mean-zero stationary Gaussian AR(p) time series.

Usage

SimulateGaussianAR(phi, n = 100, InnovationVariance = 1)

Arguments

phi

vector containing AR coefficients

n

length of time series

InnovationVariance

innovation variance

Value

A vector of length n, the simulated series

Details

The p initial values are simulated using the appropriate multivariate distribution as was suggested in McLeod (1975). The R function rnorm() is used.

References

McLeod, A.I. (1975), Derivation of the theoretical autocorrelation function of autoregressive moving-average time series, Applied Statistics 24, 255--256. Percival, D.B. and Walden, A.T. (1993), Spectral Analysis for Physical Applications.

See Also

Boot.FitAR

Examples

#Percival and Walden (1993, p.46) illustrated a time series with a 
#very peaked spectrum with the AR(4) with coefficients
#c(2.7607,-3.8106,2.6535,-0.9238) with NID(0,1) innovations.
#
z<-SimulateGaussianAR(c(2.7607,-3.8106,2.6535,-0.9238),1000)
library(lattice)
TimeSeriesPlot(z)