Strict stationarity refers to a property of a stochastic process where the joint distribution of any collection of random variables remains unchanged when shifted in time. This means that the statistical properties of the process, such as mean and variance, do not depend on time and remain constant across different time intervals. This concept is vital for understanding how certain statistical methods and models behave over time, especially in relation to the analysis of time series data and its autocorrelation structure.
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