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Time aggregation and causality tests: results from a monte carlo experiment


  • S. Cunningham
  • J. Vilasuso


This paper examines the importance of time aggregation in causality testing. We find that temporal aggregates are between two and ten times more unlikely to detect a true causal relationship than are systematic sampled aggregates over short aggregation spans.

Suggested Citation

  • S. Cunningham & J. Vilasuso, 1995. "Time aggregation and causality tests: results from a monte carlo experiment," Applied Economics Letters, Taylor & Francis Journals, vol. 2(10), pages 403-405.
  • Handle: RePEc:taf:apeclt:v:2:y:1995:i:10:p:403-405
    DOI: 10.1080/758519001

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    Cited by:

    1. 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.
    2. Bolkesjø, Torjus F. & Buongiorno, Joseph, 2006. "Short- and long-run exchange rate effects on forest product trade: Evidence from panel data," Journal of Forest Economics, Elsevier, vol. 11(4), pages 205-221, January.
    3. Rajaguru, Gulasekaran, 2004. "Impact of systematic sampling on causality in the presence of unit roots," Economics Letters, Elsevier, vol. 84(1), pages 127-132, July.
    4. Gulasekaran Rajaguru & Michael O’Neill & Tilak Abeysinghe, 2018. "Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?," Econometrics, MDPI, Open Access Journal, vol. 6(2), pages 1-24, June.

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