{"id":2720,"date":"2024-11-16T20:00:48","date_gmt":"2024-11-16T12:00:48","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=2720"},"modified":"2024-12-28T23:09:00","modified_gmt":"2024-12-28T15:09:00","slug":"he-jing-chen-s-x-2016-testing-super-diagonal-structure-in-high-dimensional-covariance-matrices-journal-of-econometrics-vol-194-pp-283-297","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/en\/publications-en\/publications-all-en\/2720\/","title":{"rendered":"He, J., &#038; Chen, S. X. (2016). Testing super-diagonal structure in high dimensional covariance matrices. Journal of Econometrics, 194, 283-297."},"content":{"rendered":"<p>The covariance matrices are essential quantities in econometric and statistical applications including port- folio allocation, asset pricing and factor analysis. Testing the entire covariance under high dimensionality endures large variability and causes a dilution of the signal-to-noise ratio and hence a reduction in the power. We consider a more powerful test procedure that focuses on testing along the super-diagonals of the high dimensional covariance matrix, which can infer more accurately on the structure of the co- variance. We show that the test is powerful in detecting sparse signals and parametric structures in the covariance. The properties of the test are demonstrated by theoretical analyses, simulation and empirical studies.<\/p>\r\n\r\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" style=\"width: 100%; height: 600px;\" data=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/\u4f55\u5a67-Chen-S.-X.-2016.-Testing-super-diagonal-structure-in-high-dimensional-covariance-matrices.pdf\" type=\"application\/pdf\" width=\"300\" height=\"150\" aria-label=\"\u5d4c\u5165 &lt;strong&gt;&lt;mark style=&quot;background-color:rgba(0, 0, 0, 0)&quot; class=&quot;has-inline-color has-vivid-cyan-blue-color&quot;&gt;Testing super-diagonal structure in high dimensional covariance matrices.pdf&lt;\/mark&gt;&lt;\/strong&gt;\"><\/object><a id=\"wp-block-file--media-60dd9b38-4940-43c9-9239-93e2bbd989d8\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/\u4f55\u5a67-Chen-S.-X.-2016.-Testing-super-diagonal-structure-in-high-dimensional-covariance-matrices.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\" style=\"background-color: rgba(0, 0, 0, 0);\">Testing super-diagonal structure in high dimensional covariance matrices.pdf<\/mark><\/strong><\/a><a class=\"wp-block-file__button wp-element-button\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/\u4f55\u5a67-Chen-S.-X.-2016.-Testing-super-diagonal-structure-in-high-dimensional-covariance-matrices.pdf\" download=\"\" aria-describedby=\"wp-block-file--media-60dd9b38-4940-43c9-9239-93e2bbd989d8\">Download<\/a><\/div>","protected":false},"excerpt":{"rendered":"<p>The covariance matrices are essential quantities in econometric and statistical applications including port- folio allocation, asset pricing and factor analysis. Testing the entire covariance under high dimensionality endures large variability and causes a dilution of the signal-to-noise ratio and hence a reduction in the power. We consider a more powerful test procedure that focuses on testing along the super-diagonals of the high dimensional covariance matrix, which can infer more accurately on the structure of the co- variance. We show that the test is powerful in detecting sparse signals and parametric structures in the covariance. The properties of the test are demonstrated by theoretical analyses, simulation and empirical studies.<\/p>\n","protected":false},"author":1,"featured_media":3125,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-2720","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications-all-en"],"acf":[],"lang":"en","translations":{"en":2720,"cn":3711},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2720","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=2720"}],"version-history":[{"count":5,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2720\/revisions"}],"predecessor-version":[{"id":3927,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2720\/revisions\/3927"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media\/3125"}],"wp:attachment":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media?parent=2720"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=2720"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=2720"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}