Comparing spectral densities of stationary time series with unequal sample sizes
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Date
2011-09-09
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Abstract
This paper deals with the comparison of several stationary processes with unequal sample sizes. We provide a detailed theoretical framework on the testing problem for equality of spectral
densities in the bivariate case, but also present the generalization to the m dimensional case and to other statistical applications like testing for zero correlation or clustering of time series data with different length. We prove asymptotic normality of an appropriately standardized version of the
test statistic both under the null and the alternative and investigate the finite sample properties of our method in a comprehensive simulation study. Furthermore we apply our approach to cluster financial time series data with different sample length.
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Keywords
cluster analysis, integrated periodogram, spectral density, stationary process, time series, unequal length