Systematic sampling and temporal aggregation in time series models

By A. Weiss. 1984

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Citations in the corpus

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).”
Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, methodology): “The method that we adopt to derive the generating mechanism for y at LF is similar to that introduced by Brewer (1973), refined by Wei (1981) and Weiss (1984), and further extended by Marcellino (1999) to deal with general aggregation schemes and multivariate processes.”
Temporal aggregation of univariate and multivariate time series models: a survey (2008, unknown): “The following result stems from Weiss (1984).”
Network modelling methods for FMRI (2011, unknown): “The spurious causality estimation that is still seen in the absence of HRF variability most likely relates to various problems described in the Granger literature (Tiao andWei, 1976;Wei, 1978;Weiss, 1984;Nalatore et al., 2007; Nolte et al., 2008); it is known that measurement noise can reverse the e”
Improving forecasting by estimating time series structural components across multiple frequencies (2014, methodology): “The consequences of temporal aggregation and systematic sampling, in terms of lag lengths (Brewer, 1973) and dynamic relationships between variables (Weiss, 1984), have also been investigated, giving useful theoretical insights on the stability of ARMA and ARMAX models.”
Discovering Temporal Causal Relations from Subsampled Data (2015, background): “As claimed in (Weiss, 1984), " some care needs to be taken in causality testing, as causality is defined for the true processes and not for the equation on the (temporally) aggregated or sampled data.”
Temporal aggregation and spurious instantaneous causality in multiple time series models (2002, background): “Other important contributions include Zellner and Montmarquette (1971), Stram and Wei (1986), Lütkepohl (1987), Weiss (1984), and Marcellino (1999), to name but a few.”