ickestationära

Ickestationära is a term used in statistics and econometrics to describe time series or stochastic processes whose statistical properties are not constant over time. In practice, non-stationarity means that features such as the mean, variance, or autocorrelation structure change with time, making standard modeling and forecasting more challenging.

Non-stationarity can arise from deterministic trends or seasonal patterns, as well as from stochastic trends produced by unit roots or evolving structural changes. Deterministic non-stationarity includes a persistent linear or nonlinear trend; stochastic non-stationarity includes random-walk-like behavior where shocks have permanent effects. A series may also exhibit time-varying volatility, a form of non-stationarity in variance.

Non-stationary data can lead to biased or spurious inferences if not properly addressed. For example, regressing non-stationary series on each other can yield high correlation without a meaningful relationship unless the series are cointegrated.

Common methods to handle non-stationarity include differencing the data to obtain stationarity (integrated processes denoted I(d)), detrending or applying transformations such as logarithms or Box-Cox, and using models that explicitly account for trends and seasonality (ARIMA, SARIMA). For multi-series data, cointegration and error-correction models are used. Structural-break models and state-space or local level models can handle breaks and evolving dynamics.

Applications of the concept are widespread in economics, finance, climatology, and other fields where long-run trends or changing conditions affect data-generating processes. While many financial prices are non-stationary, their returns are often stationary, illustrating the practical implications of non-stationarity for modeling and forecasting.