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Recognizing Overdifferenced Time Series

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  • Ming Chun Chang
  • David A. Dickey

Abstract

. Differencing is often used to render a time series stationary. The decision of how much differencing to do is usually based on plots of data, the autocorrelation function or a statistical test. Hence, it may happen that an analyst mistakenly differences a stationary series. When that happens, the inverse autocorrelation function takes on a specific pattern. We characterize this pattern and discuss the behavior of sample estimates of the inverse autocorrelation function for such overdifferenced series.

Suggested Citation

  • Ming Chun Chang & David A. Dickey, 1994. "Recognizing Overdifferenced Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(1), pages 1-18, January.
    • Handle: RePEc:bla:jtsera:v:15:y:1994:i:1:p:1-18
    DOI: 10.1111/j.1467-9892.1994.tb00173.x
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    Cited by:

    1. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Inference in Non-stationary High-Dimensional VARs," Papers 2302.01434, arXiv.org, revised Sep 2023.
    2. Oliveira, Tiago & Varum, Celeste & Botelho, Anabela, 2019. "Econometric modeling of CO2 emissions abatement: Comparing alternative approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 310-322.

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