I read from this site:
https://www.statisticshowto.com/stationarity/#:~:text=First%252Dorder%2520stationarity%2520series%2520have,doesn%2527t%2520change%2520with%2520time.
that stationarity has many types,
e.g.
Strict stationarity means that the joint distribution of any moments of any degree (e.g. expected values, variances, third order and higher moments) within the process is never dependent on time. This definition is in practice too strict to be used for any real-life model.
First-order stationarity series have means that never changes with time. Any other statistics (like variance) can change.
Which type of stationarity do the Augmented Dickey Fuller and the Phillips Perron tests capture? Do they concern both the mean and the variance or only the mean?
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