non-stationary AR(10)

[This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

In 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

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 about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)