Partial autocorrelation measures the relationship between a time series and its own past values while controlling for the effects of intervening values. This is important for identifying the appropriate lag structure in time series models, helping to distinguish between direct and indirect correlations. Understanding partial autocorrelation allows analysts to assess the influence of specific lags without being confounded by others.
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