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Correlograms for non-stationary autoregressions

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Analysis of time series often involves correlograms and partial correlograms as graphical descriptions of temporal dependence. Two methods are available for computing these statistics: one based on autocorrelations and the other on scaled autocovariances. 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.
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Keywords: Autoregression; Correlogram; Covariogram; Non-stationarity; Partial correlogram

Document Type: Research Article

Affiliations: University of Oxford, UK

Publication date: 2006-09-01

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