<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Minchul Shin | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/minchul-shin/</link><description>Minchul Shin</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/minchul-shin/index.xml" rel="self" type="application/rss+xml"/><item><title>Inference Based on Time-Varying SVARs Identified with Sign Restrictions</title><link>https://macropaperwarehouse.com/papers/inference-based-on-time-varying-svars-identified-with-sign-restrictions/</link><guid>https://macropaperwarehouse.com/papers/inference-based-on-time-varying-svars-identified-with-sign-restrictions/</guid><description>&lt;p&gt;&lt;strong&gt;Research Question.&lt;/strong&gt; The paper asks how to conduct valid Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions, a setting in which existing algorithms are shown to be theoretically flawed. As an empirical illustration, the authors use the new framework to examine three questions about the 2022–2023 Federal Reserve tightening cycle: (i) how did the Fed respond to the state of the economy; (ii) how would more dovish or hawkish stances have fared; and (iii) was the Fed behind the curve in 2021, and at what cost?&lt;/p&gt;</description></item></channel></rss>