<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>High‑Dimensional Econometrics |</title><link>https://www.khaichiong.com/tags/highdimensional-econometrics/</link><atom:link href="https://www.khaichiong.com/tags/highdimensional-econometrics/index.xml" rel="self" type="application/rss+xml"/><description>High‑Dimensional Econometrics</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 01 Aug 2021 00:00:00 +0000</lastBuildDate><image><url>https://www.khaichiong.com/media/icon_hu_da05098ef60dc2e7.png</url><title>High‑Dimensional Econometrics</title><link>https://www.khaichiong.com/tags/highdimensional-econometrics/</link></image><item><title>Estimation of High‑Dimensional Seemingly Unrelated Regression Models</title><link>https://www.khaichiong.com/publications/high-dimensional-sur-models/</link><pubDate>Sun, 01 Aug 2021 00:00:00 +0000</pubDate><guid>https://www.khaichiong.com/publications/high-dimensional-sur-models/</guid><description>&lt;p&gt;Tan, Chiong, and Moon extend the classical seemingly unrelated regression
framework to high‑dimensional settings. Their regularization‑based estimator
allows researchers to handle many equations simultaneously while capturing
dependence across them. The results have implications for macroeconomics,
finance, and marketing analytics.&lt;/p&gt;</description></item></channel></rss>