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

1
Bond Business School, Bond University, Robina, QLD 4226, Australia
2
Department of Economics, National University of Singapore, Singapore 117570, Singapore
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Author to whom correspondence should be addressed.
Econometrics 2018, 6(2), 31; https://doi.org/10.3390/econometrics6020031
Received: 17 March 2018 / Revised: 6 June 2018 / Accepted: 11 June 2018 / Published: 15 June 2018
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. View Full-Text
Keywords: systematic sampling; granger causality; cross covariance; high frequency financial data systematic sampling; granger causality; cross covariance; high frequency financial data
MDPI and ACS Style

Rajaguru, G.; O’Neill, M.; Abeysinghe, T. Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data? Econometrics 2018, 6, 31.

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