R help - Aug 2021 - Augmented Dickie-Fuller test

Hello: I've downloaded this dataset, and when I plot it it is clearly
non-stationary


df <- read.csv('
https://raw.githubusercontent.com/ourcodingclub/CC-time-series/master/monthly_milk.csv
')

plot(df,type="l")

But when I apply the Augmented Dickie-Fuller Test I get a p value of 0.01,
implying that there is evidence to reject the null that the series is
non-stationary. I am puzzled as to why this is happening. Is this because
the confidence level is basically too high or is something else going on?

adf.test(df[,2])

Augmented Dickey-Fuller Test

data: df[, 2] Dickey-Fuller = -9.9714, Lag order = 5, p-value = 0.01
alternative hypothesis: stationary

Thanks Nick Wray

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Your series shows a strong seasonal pattern that looks fairly constant.   I
would think that the adf style test is not appropriate.  What are you
trying to achieve?


John C Frain
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On Thu, 26 Aug 2021 at 14:24, Nick Wray <nickmwray at gmail.com> wrote:
> Hello: I've downloaded this dataset, and when I plot it it is clearly
> non-stationary
>
>
> df <- read.csv('
>
>
https://raw.githubusercontent.com/ourcodingclub/CC-time-series/master/monthly_milk.csv
> ')
>
> plot(df,type="l")
>
> But when I apply the Augmented Dickie-Fuller Test I get a p value of 0.01,
> implying that there is evidence to reject the null that the series is
> non-stationary. I am puzzled as to why this is happening. Is this because
> the confidence level is basically too high or is something else going on?
>
> adf.test(df[,2])
>
> Augmented Dickey-Fuller Test
>
> data: df[, 2] Dickey-Fuller = -9.9714, Lag order = 5, p-value = 0.01
> alternative hypothesis: stationary
>
> Thanks Nick Wray
>
>         [[alternative HTML version deleted]]
>
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