The argument 'x' of adf.test should not only be a vector but also a
numeric vector (see ?adf.test). Have a closer look at your data and
how they are stored. What does is.numeric(x) give? I guess that
originally your data are stored as a data frame. Converting a
data.frame to a vector is not done properly by c(), as the following
example shows:
z <- rnorm(289)
x <- data.frame(z)
adf.test(x,k=1) # produces an error
y <- c(x)
is.numeric(y) # FALSE
adf.test(y,k=1) # produces an error
k <- 1
y <- x[,1] # or y <- x$z
adf.test(y,k=1) # this works well
At 10:31 20.03.2009, you wrote:
Hi all,
I tried to do a Dickey Fuller test with R using adf.test with a time
series of german stock prices. I have 10 stocks from 1985 to 2009
with monthly stock prices. So if you do the math I have 289 values
for each stock.
I tried to do the test for each stock alone and had the 289 values
of my first stock listed in R.
When I tried to do the test with command adf.test(x, k=1) the
following warning displayed:
Fehler in embed(y, k) : 'x' is not a vector or matrix (Fehler means error)
so I tried to write x as a vector with command: z<-c(x)
I retried the test with adf.test(z, k=1) and the following error appeared:
Fehler in embed(y, k) : wrong embedding dimension
I tried quite a lot of other things but I cannot get why the test
isn't working.
I'm really no expert on this matter so please help me out as the
test if the time series is stationary is crucial for my work.
Thank you very much indeed
Matthias
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.