vw <- scan(file="U:/.www/Stat520_07/m-vw2697.txt")
length(vw)
acf(vw,lag=20)
acf(log(vw+1),lag=20)
ar3.t <- ar(vw,method="ols",order.max=3)
ar3.t$order
[1] 3
ar3.t$ar
, , 1

            [,1]
[1,]  0.10406023
[2,] -0.01027165
[3,] -0.12041467

vw.subset <- vw[1:858]
ar4.t <- ar(vw.subset,max.order=3,method="ols")
predict(ar4.t,n.ahead=6)
$pred
Time Series:
Start = 859 
End = 864 
Frequency = 1 
[1]  0.004145742 -0.001255294  0.011948908  0.014418122  0.010277379
[6]  0.006661663

$se
Time Series:
Start = 859 
End = 864 
Frequency = 1 
[1] 0.05404047 0.05439112 0.05439147 0.05483723 0.05486091 0.05501077

vw[859:864]
[1]  0.076227 -0.036511  0.058017 -0.034077  0.031135  0.018313
plot(1:6,vw[859:864])
lines(1:6,predict(ar4.t,n.ahead=6)$pred)
plot(1:6,vw[859:864],type="l")
lines(1:6,predict(ar4.t,n.ahead=6)$pred)