Temporal Disaggregation Using Multivariate Structural Time Series Models

By Filippo Moauro, Giovanni Savio. 2005

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Causal Discovery from Temporally Aggregated Time Series (2017, background): “…disaggregation of low resolution time series has been extensively studied in the econometric and statistical literature (Boot et al., 1967; Stram & Wei, 1986; Harvey & Chung, 2000; Moauro & Savio, 2005; Proietti, 2006), which is clearly an even harder problem than discovering causal relations.”
Discovering Temporal Causal Relations from Subsampled Data (2015, background): “There has been plenty of work in economics for temporal disaggregation of the low-resolution time series, with or without the side information from related indicators observed at the desired high frequency (Harvey & Chung, 2000; Moauro & Savio, 2005; Proietti, 2006).”
Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series. (2017, background): “For example, many econometric indicators, such as GDP and housing price data, are recorded at quarterly and monthly scales [1].”
Granger Causality: A Review and Recent Advances (2021, background): “For example, many econometric indicators, such as GDP and housing price data, are recorded at quarterly and monthly scales (Moauro and Savio, 2005), but important interactions between these indicators may occur weekly or bi-weekly (Moauro and Savio, 2005; Stram and Wei, 1986; Boot et al., 1967).”