Strict stationaritymeans that thejoint distributionof anymomentsof any degree (e.g.expected values,variances, third order and higher moments) within the process isneverdependent on time. This definition is in practice too strict to be used for any real-life model.
First-order stationarityseries have means that never changes with time. Any other statistics (like variance)canchange.
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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