Temporal aggregation of univariate and multivariate time series models: a survey

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Causal Discovery from Temporally Aggregated Time Series (2017, background): “There are two typical schemes to generate low-resolution or low-frequency data from high-frequency ones (Silvestrini & Veredas, 2008; Marcellino, 1999).”
Granger Causality: A Review and Recent Advances (2021, background): “A causal analysis at a slower time scale than the true causal time scale may miss true interactions and add spurious ones (Zhou et al., 2014; Silvestrini and Veredas, 2008; Boot et al., 1967; Breitung and Swanson, 2002).”
Discovering Temporal Causal Relations from Subsampled Data (2015, background): “See Silvestrini & Veredas (2008) for a survey on aggregation of univariate and multivariate time series models.”
Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series. (2017, background): “A causal analysis rooted at a slower time scale than the true causal time scale may both miss true interactions and add spurious ones [3, 4, 5, 6].”