Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230793
Authors: 
Xiao, Han
Wu, Wei Biao
Year of Publication: 
2019
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2019-017
Abstract: 
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.
Subjects: 
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
JEL: 
C00
Document Type: 
Working Paper

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