Temporal Disaggregation By State Space Methods: Dynamic Regression Methods Revisited

By Tommaso Proietti. 2006

101 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

How researchers see this paper

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In Control and Systems Engineering

1. Bernhard Schölkopf
2. Clark Glymour
3. Kun Zhang
4. Mingming Gong
5. Dacheng Tao
6. Dominik Janzing
7. Jan Peters
8. Joris M. Mooij

Citations in the corpus

• Causal Discovery from Temporally Aggregated Time Series (2017, background): “Temporal 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 relatio”
• 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).”
• Survey Data as Coincident or Leading Indicators (2010, unknown): “The hypothesis of stationarity might be relaxed (see Proietti (2006)) and the ADL model could be estimated in first differences:”