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

Gulasekaran Rajaguru,Tilak Abeysinghe,Michael O’Neill

doi:10.3390/econometrics6020031

Gulasekaran Rajaguru, Tilak Abeysinghe + Show 1 more

Open Access

https://doi.org/10.3390/econometrics6020031

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Journal: Econometrics	Publication Date: Jun 15, 2018
Citations: 4	License type: CC BY 4.0

Affiliation: Bond University, National University of Singapore

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.

Highlights

The use of highly temporally aggregated and systematically sampled data for causal inference is quite common in the applied econometric literature
Φ2,21 and φ1,12 at the 5% level of significance are reported in panel B of Table 1. It is clear from the results reported in panel B of Table 1 that the systematic sampling induces spurious bidirectional Granger causality when the underlying data generating process is VAR(2) regardless of order of integration
The results show that the only case where the direction of Granger causality is preserved is when all variables are stationary

Summary

Introduction

The use of highly temporally aggregated and systematically sampled data for causal inference is quite common in the applied econometric literature. Breitung and Swanson (2002) examine how the spurious instantaneous relations are induced from Granger causal relationships due to temporal aggregation and systematic sampling in a VAR framework. Using the VAR(1) system in (12) with φ11 = 0 and φ22 = 0, Breitung and Swanson (2002) and Ericsson et al (2001) examined the effect of temporal aggregation on contemporaneous regression coefficient for m = 2 and observed that this coefficient could be positive, negative, or zero We generalize their result for the case of systematic sampling for any m. It can be shown that if the one-sided causality runs from a white noise series (in differences) to a differenced stationary series in the basic disaggregated form systematic sampling will not produce a spurious feedback relationship even if d1 = 1. The results are consistent with Breitung and Swanson (2002)

Monte Carlo Simulation

Empirical Applications

Findings

Conclusions