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

Gulasekaran Rajaguru, Michael O'Neill, Tilak Abeysinghe

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)
33 Downloads (Pure)

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.
Original languageEnglish
Article number31
Number of pages24
JournalEconometrics
Volume6
Issue number2
DOIs
Publication statusPublished - 15 Jun 2018

Fingerprint

Financial data
Granger causality
Sampling
Causality
Causal inference
Applied econometrics
Exercise
Usefulness

Cite this

@article{25e41d537bb848d79b34445460d0daea,
title = "Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?",
abstract = "In applied econometric literature, the causal inferences are often made based on temporallyaggregated or systematically sampled data. A number of studies document that temporal aggregationhas 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.",
author = "Gulasekaran Rajaguru and Michael O'Neill and Tilak Abeysinghe",
year = "2018",
month = "6",
day = "15",
doi = "10.3390/econometrics6020031",
language = "English",
volume = "6",
journal = "Econometrics",
issn = "2225-1146",
publisher = "MDPI",
number = "2",

}

Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data? / Rajaguru, Gulasekaran; O'Neill, Michael; Abeysinghe, Tilak.

In: Econometrics, Vol. 6, No. 2, 31, 15.06.2018.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?

AU - Rajaguru, Gulasekaran

AU - O'Neill, Michael

AU - Abeysinghe, Tilak

PY - 2018/6/15

Y1 - 2018/6/15

N2 - In applied econometric literature, the causal inferences are often made based on temporallyaggregated or systematically sampled data. A number of studies document that temporal aggregationhas 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.

AB - In applied econometric literature, the causal inferences are often made based on temporallyaggregated or systematically sampled data. A number of studies document that temporal aggregationhas 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.

UR - http://www.scopus.com/inward/record.url?scp=85055104412&partnerID=8YFLogxK

U2 - 10.3390/econometrics6020031

DO - 10.3390/econometrics6020031

M3 - Article

VL - 6

JO - Econometrics

JF - Econometrics

SN - 2225-1146

IS - 2

M1 - 31

ER -