{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:28:32Z","timestamp":1780392512539,"version":"3.54.1"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T00:00:00Z","timestamp":1554681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,6,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1093\/biomet\/asz007","type":"journal-article","created":{"date-parts":[[2019,2,18]],"date-time":"2019-02-18T04:22:18Z","timestamp":1550463738000},"page":"433-452","source":"Crossref","is-referenced-by-count":14,"title":["Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series"],"prefix":"10.1093","volume":"106","author":[{"given":"A","family":"Tank","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Washington, Box 354322, Seattle, Washington 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E B","family":"Fox","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Washington, Box 354322, Seattle, Washington 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Shojaie","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Washington, Box 357232, Seattle, Washington 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,4,8]]},"reference":[{"key":"2020040720052461400_B1","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1017\/S0266466615000043","article-title":"Multivariate AR systems and mixed-frequency data: G-identifiability and estimation","volume":"32","author":"Anderson,","year":"2016","journal-title":"Economet. 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