<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
	<channel>
		<title>Monetary-Policy on r-econometrics</title>
		<link>https://www.r-econometrics.com/tags/monetary-policy/</link>
		<description>Recent content in Monetary-Policy on r-econometrics</description>
		<generator>Hugo</generator>
		<language>en-us</language>
		
		
		
		
			<lastBuildDate>Mon, 12 Oct 2026 00:00:00 +0000</lastBuildDate>
		
			<atom:link href="https://www.r-econometrics.com/tags/monetary-policy/index.xml" rel="self" type="application/rss+xml" />
			<item>
				<title>An Introduction to Factor-Augmented Vector Autoregression (FAVAR)</title>
				<link>https://www.r-econometrics.com/timeseries/favarintro/</link>
				<pubDate>Mon, 12 Oct 2026 00:00:00 +0000</pubDate>
				<guid>https://www.r-econometrics.com/timeseries/favarintro/</guid>
				<description>&lt;div id=&#34;introduction&#34; class=&#34;section level2&#34;&gt;&#xA;&lt;h2&gt;Introduction&lt;/h2&gt;&#xA;&lt;p&gt;As we saw in the &lt;a href=&#34;https://www.r-econometrics.com/timeseries/varintro&#34;&gt;post on vector autoregressive (VAR) models&lt;/a&gt;, traditional VAR models suffer from a so-called &lt;em&gt;curse of dimensionality&lt;/em&gt;. Each additional variable adds a further equation and a further lag polynomial to every other equation, so the number of coefficients grows with the square of the number of variables. With the few decades of quarterly data that are usually available this limits a VAR to a handful of variables. Policy makers, however, base their decisions on a much larger set of indicators than a VAR can accommodate, and leaving that information out of the model can distort the results.&lt;/p&gt;</description>
			</item>
	</channel>
</rss>
