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Temporal Aggregation, Causality Distortions, and a Sign Rule

Author

Listed:
  • Tilak Abeysinghe

    () (National University of Singapore)

  • Gulasekaran Rajaguru

    (Bond University)

Abstract

Temporally aggregated data is a bane for Granger causality tests. The same set of variables may lead to contradictory causality inferences at different levels of temporal aggregation. Obtaining temporally disaggregated data series is impractical in many situations. Since cointegration is invariant to temporal aggregation and implies Granger causality this paper proposes a sign rule to establish the direction of causality. Temporal aggregation leads to a distortion of the sign of the adjustment coefficients of an error correction model. The sign rule works better with highly temporally aggregated data. The practitioners, therefore, may revert to using annual data for Granger causality testing instead of looking for quarterly, monthly or weekly data. The method is illustrated through three applications.

Suggested Citation

  • Tilak Abeysinghe & Gulasekaran Rajaguru, 2003. "Temporal Aggregation, Causality Distortions, and a Sign Rule," Departmental Working Papers wp0406, National University of Singapore, Department of Economics.
  • Handle: RePEc:nus:nusewp:wp0406
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    References listed on IDEAS

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    Cited by:

    1. Girardin, Eric & Liu, Zhenya, 2007. "The financial integration of China: New evidence on temporally aggregated data for the A-share market," China Economic Review, Elsevier, vol. 18(3), pages 354-371.

    More about this item

    Keywords

    Granger causality test; cointegration; error correction model; adjustment coefficient; sign rule;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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