Temporal aggregation, cointegration and causality inference

31 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Causal Discovery from Temporally Aggregated Time Series (2017, background): “Numerous contributions have been made on analyzing the effects of the above two schemes to generate low-resolution data on the properties of the time series such as estimated causal relations and exogeneity (Tiao, 1972; Weiss, 1984; Granger, 1987; Marcellino, 1999; Rajaguru & Abeysinghe, 2008).”
Discovering Temporal Causal Relations from Subsampled Data (2015, background): “" Various contributions have been made on how temporal aggregation changes the Granger causal relations in the data (Rajaguru & Abeysinghe, 2008; Breitung & Swanson, 2002); for instance , temporal aggregation could cause spurious instantaneous correlations in the time series.”