{"id":3711,"date":"2024-11-16T20:00:48","date_gmt":"2024-11-16T12:00:48","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=3711"},"modified":"2024-12-28T23:08:47","modified_gmt":"2024-12-28T15:08:47","slug":"he-j-chen-s-x-2016-testing-super-diagonal-structure-in-high-dimensional-covariance-matrices-journal-of-econometrics-1942-283-297","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/publications\/publications-all\/3711\/","title":{"rendered":"\u4f55\u5a67, &#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\">\u4e0b\u8f7d<\/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":[15],"tags":[],"class_list":["post-3711","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications-all"],"acf":[],"lang":"cn","translations":{"cn":3711,"en":2720},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/3711","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=3711"}],"version-history":[{"count":4,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/3711\/revisions"}],"predecessor-version":[{"id":3926,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/3711\/revisions\/3926"}],"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=3711"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=3711"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=3711"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}