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

SUBMITTER: Tank A 

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

REPOSITORIES: biostudies-literature