The paper presents a systematic theory for asymptotic inferences based on autocovariances of stationary processes. We consider nonparametric tests for se rial correlations using the maximum and the quadratic deviations of sample autocovariances. For these cases, with proper centering and rescaling, the asymptotic distributions of the deviations are Gumbel and Gaussian, respec tively. To establish such an asymptotic theory, as byproducts, we develop a normal comparison principle and propose a sufficient condition for summability of joint cumulants of stationary processes. We adapt a blocks of blocks bootstrapping procedure proposed by Kuensch (1989) and Liu and Singh (1992) to the maximum deviation based tests to improve the finite-sample performance.
Autocovariance blocks of blocks bootstrapping Box-Pierce test extreme value distribution moderate deviation normal comparison physical dependence measure short range dependence stationary process summability of cumulants