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

A Tank, E B Fox and A Shojaie

Biometrika, 2019, vol. 106, issue 2, 433-452

Abstract: SummaryCausal 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.

Keywords: Mixed frequency; Non-Gaussian error; Structural vector autoregressive model; Subsampling; Time series (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (11)

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