**I**n the revision of **Bayesian Core** on which Jean-Michel Marin and I worked together most of last week, having missed our CIRM break last summer (!), we have now included an illustration of what happens to an AR(p) time series when the customary stationarity+causality condition on the roots of the associated polynomial is not satisfied. More specifically, we generated several time-series with the same underlying white noise and random coefficients that have a fair chance of providing non-stationary series and then plotted the 260 next steps of the series by the R code

p=10
T=260
dat=seri=rnorm(T) #white noise
par(mfrow=c(2,2),mar=c(2,2,1,1))
for (i in 1:4){
coef=runif(p,min=-.5,max=.5)
for (t in ((p+1):T))
seri[t]=sum(coef*seri[(t-p):(t-1)])+dat[t]
plot(seri,ty="l",lwd=2,ylab="")
}

leading to outputs like the following one

Filed under: Books, R, Statistics, University life Tagged: AR(p) model, Bayesian Core, polynomials, R, stationarity, time series

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** Xi'an's Og » R**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...

**Tags:** AR(p) model, Bayesian Core, Books, polynomials, R, Stationarity, statistics, Time series, University life