Further Methods of Derivation of Quarterly Figures from Annual Data
By J. Boot, W. Feibes, J. Lisman. 1967
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- Kun Zhang
- Bernhard Schölkopf
- Mingming Gong
- Dacheng Tao
- Clark Glymour
- Jan Peters
- Ali Shojaie
- Fotios Petropoulos
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…”
- 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].”
- Discovering Temporal Causal Relations from Subsampled Data (2015, methodology): “We compared our methods to two classical time series disaggregation methods: the Boot-Feibes-Lisma (BFL) method (Boot et al., 1967) and Stram-Wei (SW) method (Stram & Wei, 1986).”
- A systemic view of the ADIDA framework (2014, background): “Temporal aggregation can be reversed through disaggregation schemes (e.g. Boot et al., 1967; Chan, 1993; Feijoó et al., 2003; Lisman & Sandee, 1964; Wei & Stram, 1990).”
- The Volatility of Capital Flows in Emerging Markets: Measures and Determinants (2017, methodology): “Nominal GDP: 1970Q1-2016Q4 from IFS; missing quarterly data have been extrapolated from the annual series using the disaggregation technique of Boot et al. (1967); non-seasonally adjusted series have been adjusted using the US Census’ X13 program.”
- 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).”