{"id":6267,"date":"2026-09-18T17:30:26","date_gmt":"2026-09-18T09:30:26","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=6267"},"modified":"2026-09-29T15:59:50","modified_gmt":"2026-09-29T07:59:50","slug":"hes-paper-accepted-by-joe","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/en\/latest-news-en\/6267\/","title":{"rendered":"He&#8217;s Paper Accepted by JOE"},"content":{"rendered":"<p>The paper \u201cSpatio-temporal Autoregressions for High-Dimensional Matrix-Valued Time Series\u201d, co-authored by Assistant Prof. Baojun Dou of City University of Hong Kong, Prof. Jing He from our team, Sudhir Tiwari of Macquarie Group, and Prof. Qiwei Yao of the London School of Economics and Political Science, has been officially accepted by the <em>Journal of Econometrics<\/em>.<\/p>\n<p style=\"text-align: center;\"><span style=\"color: #0e57a0;\"><strong>Abstract<\/strong><\/span><\/p>\n<p>Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals across multiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions across multiple dimensions. The inherent endogeneity in spatio-temporal autoregressive models renders ordinary least squares estimation inconsistent. To overcome this difficulty while simultaneously estimating two distinct weight matrices with banded structure, we develop an iterated generalized Yule-Walker estimator by adapting a generalized method of moments framework based on Yule-Walker equations. Moreover, unlike conventional models that employ a single bandwidth parameter, the dual-bandwidth specification in our framework requires a new two-step, ratio-based sequential estimation procedure.<\/p>\n<p style=\"text-align: center;\"><span style=\"color: #0e57a0;\"><strong>Author Introduction<\/strong><\/span><\/p>\n<p>Baojun Dou is an Assistant Professor at City University of Hong Kong. His main research interests include high-dimensional time series analysis, spatio-temporal data analysis, and change-point analysis.<\/p>\n<p>Jing He is a Professor at the Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics. Her main research interests include high-dimensional data analysis and spatio-temporal data analysis.<\/p>\n<p>Sudhir Tiwari is a Quantitative Portfolio Manager for statistical arbitrage strategies at Macquarie Group. He previously served as Head of Algorithmic Trading at CLSA.<\/p>\n<p>Qiwei Yao is a Chair Professor at the London School of Economics and Political Science. His main research interests include time series analysis, dimension reduction and factor modelling, dynamic network modelling, spatio-temporal modelling, financial econometrics, and nonparametric regression.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper \u201cSpatio-temporal Autoregressions for High-Dimensional Matrix-Valued Time Series\u201d, co-authored by Assistant Prof. Baojun Dou of City University of Hong Kong, Prof. Jing He from our team, Sudhir Tiwari of Macquarie Group, and Prof. Qiwei Yao of the London School of Economics and Political Science, has been officially accepted by the *Journal of Econometrics.<\/p>\n","protected":false},"author":1,"featured_media":6268,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-6267","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-latest-news-en"],"acf":[],"lang":"en","translations":{"en":6267,"cn":6266},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/6267","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/comments?post=6267"}],"version-history":[{"count":1,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/6267\/revisions"}],"predecessor-version":[{"id":6271,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/6267\/revisions\/6271"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media\/6268"}],"wp:attachment":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media?parent=6267"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=6267"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=6267"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}