Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?

Gulasekaran Rajaguru (), Michael O’Neill and Tilak Abeysinghe
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Michael O’Neill: Bond Business School, Bond University, Robina, QLD 4226, Australia

Econometrics, 2018, vol. 6, issue 2, 1-24

Abstract: In applied econometric literature, the causal inferences are often made based on temporally aggregated or systematically sampled data. A number of studies document that temporal aggregation has distorting effects on causal inference and systematic sampling of stationary variables preserves the direction of causality. Contrary to the stationary case, this paper shows for the bivariate VAR(1) system that systematic sampling induces spurious bi-directional Granger causality among the variables if the uni-directional causality runs from a non-stationary series to either a stationary or a non-stationary series. An empirical exercise illustrates the relative usefulness of the results further.

Keywords: systematic sampling; granger causality; cross covariance; high frequency financial data (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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