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.