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For a stationary time series the resulting plots are nearly identical. When it comes to time series exhibiting non-stationary features these methods can lead to very different results. This has two consequences: incorrect inferences can be drawn when confusing these concepts; better discrimination between stationary and non-stationarity is achieved when using autocorrelations instead of, or along with, the autocovariances which are commonly used in statistical software.<\/jats:p>","DOI":"10.1111\/j.1467-9868.2006.00563.x","type":"journal-article","created":{"date-parts":[[2006,7,19]],"date-time":"2006-07-19T16:08:38Z","timestamp":1153325318000},"page":"707-720","source":"Crossref","is-referenced-by-count":14,"title":["Correlograms for Non-Stationary Autoregressions"],"prefix":"10.1093","volume":"68","author":[{"given":"Bent","family":"Nielsen","sequence":"first","affiliation":[{"name":"University of Oxford , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2006,7,19]]},"reference":[{"key":"2023040708374207400_","doi-asserted-by":"crossref","first-page":"536","DOI":"10.2307\/2342284","article-title":"Some aspects of the time-correlation problem in regard to tests of significance","volume":"98","author":"Bartlett","year":"1935","journal-title":"J. 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