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The Consequences of Systematic Sampling on Granger Causality

Author

Listed:
  • Tilak Abeysinghe
  • Gulasekaran Rajaguru

Abstract

In applied econometric literature, the causal inferences are often made based on highly temporally aggregated or systematically sampled data. A number of theoretical studies have pointed out that temporal aggregation has distorting effects on causal inference and systematic sampling preserves the direction of causality. This paper examines the issue in detail by plugging in theoretical cross covariances into the limiting values of least squares estimates. The asymptotic distributions of the estimates of systematically sampled process are expressed in terms of the cross covariances of the disaggregated process. An extensive Monte Carlo study is conducted to examine small sample results. Quite contrary to the stationary case where systematic sampling preserves the direction of Granger causality, this paper shows that systematic sampling of integrated series may induce spurious causality, even if, they are used in differenced form. It is observed that in general the most distorting causal inferences are likely at low levels of aggregation where the order of sampling-span just exceeds the actual causal lag. It is found that if the uni-directional causality runs from a non-stationary series to a stationary or a non-stationary series, there is a high likelihood of detecting spurious bi-directional causality

Suggested Citation

  • Tilak Abeysinghe & Gulasekaran Rajaguru, 2004. "The Consequences of Systematic Sampling on Granger Causality," Econometric Society 2004 Australasian Meetings 250, Econometric Society.
  • Handle: RePEc:ecm:ausm04:250
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    More about this item

    Keywords

    Systematic Sampling; Granger causality; Unit Roots; Cross covariance;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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