Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/230793 
Autor:innen: 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2019-017
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
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.
Schlagwörter: 
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
Dokumentart: 
Working Paper

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