memochange: An R package for estimation procedures and tests for persistent time series

For modeling and forecasting time series it is essential to know whether the series are stationary or non-stationary since many commonly applied statistical methods (such as OLS) are invalid under non-stationarity. Two features that cause a time series to be non-stationary are considered here. On the one hand a time series can be subject to a change in mean, i.e. the expected value of the series changes over time. On the other hand a time series can be subject to a break in the autocovariance often referred to as a change in persistence, i.e. the dependence structure of the series changes over time. Potential examples for both a change in mean and a change in persistence can be found in Figure 1.

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