The distortionary effects of temporal aggregation on Granger causality

Gulasekaran Rajaguru, Tilak Abeysinghe

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Economists often have to use temporally aggregated data in causality tests. A number of theoretical studies have pointed out that temporal aggregation has distorting effects on causal inference. This paper provides a quantitative assessment of the magnitude of the distortions created by temporal aggregation by plugging in theoretical cross covariances into the limiting values of least
squares estimates. Some Monte Carlo results and an application are provided to assess the impact in small samples. It is observed that in general the most distorting causal inferences are likely at low levels of temporal aggregation. At high levels of aggregation, causal information concentrates in contemporaneous correlations. At present, a data-based approach is not available to establish
the direction of causality between contemporaneously correlated variables.
Original languageEnglish
Title of host publicationSome recent developments in statistical theory and applications
Subtitle of host publicationSelected Proceedings of the International Conference on Recent Developments in Statistics, Econometrics and Forecasting, University of Allahabad, India, December 27-28, 2010
EditorsKumar Kuldeep, Anoop Chaturvedi
Place of PublicationBoca Raton, Florida
PublisherBrown Walker Press
Number of pages19
ISBN (Print)9781612335735
Publication statusPublished - 2012
Event2001 Econometric Society Australasian Meetings - Auckland, New Zealand
Duration: 6 Jul 20019 Jul 2001


Conference2001 Econometric Society Australasian Meetings
Country/TerritoryNew Zealand


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