Author: Nielsen, Bent

Source: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 68, Number 4, September 2006 , pp. 707-720(14)

Publisher: Wiley-Blackwell

Abstract:

Summary. 

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.

Keywords: Autoregression; Correlogram; Covariogram; Non-stationarity; Partial correlogram

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9868.2006.00563.x

Affiliations: 1: University of Oxford, UK

Publication date: 2006-09-01

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