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The use of temporally aggregated data on detecting a mean change of a time series process

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  • Bu Hyoung Lee
  • William W. S. Wei

Abstract

In this article we investigate the effects of temporal aggregation on testing for a mean change of time series through a likelihood ratio (LR) test. We derive the functional relationship between non aggregate-model parameters and aggregate-model parameters. Using the relationship, we propose a modified LR test when aggregate data are used. Through the theory, Monte Carlo simulations, and empirical examples, we show that aggregation leads the null distribution of the LR test statistic being shifted to the left. Hence, the test power increases as the order of aggregation increases.

Suggested Citation

  • Bu Hyoung Lee & William W. S. Wei, 2017. "The use of temporally aggregated data on detecting a mean change of a time series process," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(12), pages 5851-5871, June.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:12:p:5851-5871
    DOI: 10.1080/03610926.2015.1091082
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    Cited by:

    1. Bu Hyoung Lee, 2022. "Bootstrap Prediction Intervals of Temporal Disaggregation," Stats, MDPI, vol. 5(1), pages 1-13, February.

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