ArticleBiometrika2019
Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series.
Article in Biometrika, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Inferring directed spectral information flow between mixed-frequency time series.Research square · 2025Article
- Granger Causality: A Review and Recent Advances.Annual review of statistics and its application · 2022Article
- Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes.Entropy (Basel, Switzerland) · 2021Article
- Amalgamating evidence of dynamics.Synthese · 2019Article
- Causal Discovery from Temporally Aggregated Time Series.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector autoregressive models. We present a unifying framework for parameter identifiability and estimation under subsampling and mixed frequencies when the noise, or shocks, is non-Gaussian. By studying the structural case, we develop identifiability and estimation methods for the causal structure of lagged and instantaneous effects at the desired time scale. We further derive an exact expectation-maximization algorithm for inference in both subsampled and mixed-frequency settings. We validate our approach in simulated scenarios and on a climate and an econometric dataset.
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Registered trials
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