<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ke-Li Xu | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/ke-li-xu/</link><description>Ke-Li Xu</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/ke-li-xu/index.xml" rel="self" type="application/rss+xml"/><item><title>Local Projection-Based Inference under General Conditions</title><link>https://macropaperwarehouse.com/papers/local-projection-based-inference-under-general-conditions/</link><guid>https://macropaperwarehouse.com/papers/local-projection-based-inference-under-general-conditions/</guid><description>&lt;p&gt;This paper develops a uniform asymptotic theory for local projection (LP) regression under general conditions, addressing a gap in the literature where existing results required restrictive assumptions about lag order, data persistence, and shock processes. The research question is: how can one conduct valid statistical inference on impulse responses from LP regressions when the true lag order is unknown (possibly infinite), data exhibit arbitrary persistence including unit roots and near-unit roots, horizons are allowed to grow with sample size, and shocks follow general conditionally heteroskedastic martingale difference sequences (MDS)?&lt;/p&gt;</description></item></channel></rss>