Hi, I am trying to test whether a series is return series stationary, but before proceeding I wanted to make sure I understand correctly how to use the adf.test function and interpret its output... Could you please let me know whether I am correct in my interpretations?
ex: I take x such as I know it doesn't have a unit root, and is therefore stationary 1/ > x <- rnorm(1000) > adf.test(x, "stationary", k=0) Augmented Dickey-Fuller Test data: x Dickey-Fuller = -31.8629, Lag order = 0, p-value = 0.01 alternative hypothesis: stationary Warning message: In adf.test(x, "stationary", k = 0) : p-value smaller than printed p-value If I understand correctly, I am told that the probability of x having a unit root and therefore being non-stationary is 0.01, so the test tells me that there is a very high probability that x is stationary. Then I can conclude that x is mean-reverting. Am I correct? 2/ I would like to see critical values also, so I tried with ur.df > summary(ur.df(x, "trend", lag=0)) <snip> Value of test-statistic is: -31.8629 338.4156 507.6231 Critical values for test statistics: 1pct 5pct 10pct tau3 -3.96 -3.41 -3.12 phi2 6.09 4.68 4.03 phi3 8.27 6.25 5.34 Here if I understand correctly, as my first critical value is significantly less than the 1% critical value I reject the null hypothesis that x has a unit root, so x is stationary and then mean reverting. Thanks, -Arnaud ______________________________________________ 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.