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Correlograms for non-stationary autoregressions

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  • Bent Nielsen

Abstract

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. Copyright 2006 Royal Statistical Society.

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Bibliographic Info

Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Volume (Year): 68 (2006)
Issue (Month): 4 ()
Pages: 707-720

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Handle: RePEc:bla:jorssb:v:68:y:2006:i:4:p:707-720

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Cited by:
  1. Bent Nielsen, 2005. "Analysis of co-explosive processes," Economics Papers 2005-W08, Economics Group, Nuffield College, University of Oxford.
  2. Karim Abadir & Giovanni Caggiano & Gabriel Talmain, 2005. "Nelson-Plosser Revisited: the ACF Approach," Working Papers, Business School - Economics, University of Glasgow 2005_7, Business School - Economics, University of Glasgow.
  3. Eric Engler & Bent Nielsen, 2007. "The empirical process of autoregressive residuals," Economics Series Working Papers 2007-W01, University of Oxford, Department of Economics.
  4. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, Elsevier, vol. 151(2), pages 140-149, August.
  5. B. Nielsen, 2009. "Test for cointegration rank in general vector autoregressions," Economics Papers 2009-W10, Economics Group, Nuffield College, University of Oxford.

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