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Time aggregation effect on the correlation coefficient: added-systematically sampled framework

Author

Listed:
  • R Jea

    (National Chiao Tung University)

  • C-T Su

    (National Tsing Hua University)

  • J-L Lin

    (The Institute of Economics, Academia Sinica, Nankang
    National Chengchi University, Mucha)

Abstract

The aggregation of financial and economic time series occurs in a number of ways. Temporal aggregation or systematic sampling is the commonly used approach. In this paper, we investigate the time interval effect of multiple regression models in which the variables are additive or systematically sampled. The correlation coefficient changes with the selected time interval when one is additive and the other is systematically sampled. It is shown that the squared correlation coefficient decreases monotonically as the differencing interval increases, approaching zero in the limit. When two random variables are both added or systematically sampled, the correlation coefficient is invariant with time and equal to the one-period values. We find that the partial regression and correlation coefficients between two additive or systematically sampled variables approach one-period values as n increases. When one of the variables is systematically sampled, they will approach zero in the limit. The time interval for the association analyses between variables is not selected arbitrarily or the statistical results are likely affected.

Suggested Citation

  • R Jea & C-T Su & J-L Lin, 2005. "Time aggregation effect on the correlation coefficient: added-systematically sampled framework," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(11), pages 1303-1309, November.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:11:d:10.1057_palgrave.jors.2601947
    DOI: 10.1057/palgrave.jors.2601947
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    References listed on IDEAS

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    Cited by:

    1. Zhaoyuan Li & Maozai Tian, 2017. "A New Method For Dynamic Stock Clustering Based On Spectral Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 373-392, October.

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