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The purpose of this study is to investigate the effects of temporal aggregation on modeling and testing a single variance-change in a univariate time series process. We show that the aggregation effect on the variance-change ARMA model can be explained only with non-aggregate model parameters, based on functional links between a non-aggregate process and its  m -aggregate process. In addition, through Monte Carlo simulations and a real data example, we compare empirical powers for the two CUSUMSQ tests—Lee and Park’s test and Inclán and Tiao’s test under given conditions of temporal aggregation and find out a better performance on the Inclán and Tiao CUSUMSQ test at moderate-level aggregation despite severe information loss on both tests at high-level aggregation.

The use of temporally aggregated data in modeling and testing a variance change in a time series