Long-run variance of a stationary process (${\sigma }_{\mathrm{LR}}^2={\sum }_h\gamma (h)$)

With absolutely summable autocovariance, the asymptotic variance of the sample mean satisfiesThe sum is signed, rather than a sum of absolute values. It can be zero: the first difference of strong white noise has telescoping partial sums. A central limit theorem needs further dependence assumptions.

When the long-run variance of a stationary process is positive, matching the variance of its average to an average of independent observations gives $T_{\mathrm{eff}}\simeq T\gamma (0)/{\sum }_h\gamma (h)$. Positive aggregate correlation reduces this size; negative aggregate correlation can increase it beyond the observation count. This extends the variance interpretation of effective sample size of a Markov chain.