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RECOGNIZING OVERDIFFERENCED TIME SERIES
Author(s) -
Chang Ming Chun,
Dickey David A.
Publication year - 1994
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1994.tb00173.x
Subject(s) - autocorrelation , series (stratigraphy) , mathematics , autocorrelation technique , partial autocorrelation function , order of integration (calculus) , statistics , time series , inverse , autocorrelation matrix , moving average model , function (biology) , econometrics , stationary process , mathematical analysis , autoregressive integrated moving average , paleontology , geometry , evolutionary biology , biology
. 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.