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Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series.

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.

SUBMITTER: Tank A

PROVIDER: S-EPMC6508036 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series.

Tank A A Fox E B EB Shojaie A A

Biometrika 20190408 2

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 ...[more]

PMID: 31097836

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Dataset Information

Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series.

Publications

Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series.

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