<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Frank Schorfheide | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/frank-schorfheide/</link><description>Frank Schorfheide</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://macropaperwarehouse.com/authors/frank-schorfheide/index.xml" rel="self" type="application/rss+xml"/><item><title>On the Effects of Monetary Policy Shocks on Income and Consumption Heterogeneity</title><link>https://macropaperwarehouse.com/papers/on-the-effects-of-monetary-policy-shocks-on-income-and-consumption-heterogeneity/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://macropaperwarehouse.com/papers/on-the-effects-of-monetary-policy-shocks-on-income-and-consumption-heterogeneity/</guid><description>&lt;p&gt;This paper asks how conventional and informational monetary policy shocks affect the cross-sectional distributions of labor earnings, consumption, and financial income in the United States. The motivation is the growing concern, particularly in the aftermath of the global financial crisis, about distributional consequences of central bank actions. Existing studies either include scalar inequality statistics in standard VARs — losing information about the full distribution — or rely on indirect approaches that hold household portfolio compositions fixed. Chang and Schorfheide instead apply the functional VAR (fVAR) framework developed in Chang, Chen, and Schorfheide (2024, JPE forthcoming) that stacks macroeconomic aggregates alongside the full time-varying cross-sectional density, represented as a log probability density function approximated via a cubic-spline sieve. This allows simultaneous, internally-consistent IRFs for percentiles, Gini coefficients, 90-10 ratios, standard deviations, and other distributional statistics without the risk of quantile crossings.&lt;/p&gt;</description></item><item><title>Optimal Decision Rules When Payoffs are Partially Identified</title><link>https://macropaperwarehouse.com/papers/optimal-decision-rules-when-payoffs-are-partially-identified/</link><guid>https://macropaperwarehouse.com/papers/optimal-decision-rules-when-payoffs-are-partially-identified/</guid><description>&lt;p&gt;This paper derives asymptotically optimal statistical decision rules for discrete choice problems when the payoffs associated with some choices are only partially identified. The research question is: how should a decision maker who can bound but not point-identify a payoff-relevant parameter θ use data to make optimal policy choices?&lt;/p&gt;
&lt;p&gt;The framework separates two parameter types. The reduced-form parameter µ is point-identified and can be estimated from data. The structural parameter θ — such as the average treatment effect (ATE) in a target population — is set-identified, meaning only that θ ∈ Θ0(µ) can be established, where the identified set is indexed by µ. The decision maker confronts both ambiguity (arising from partial identification of θ given µ) and statistical uncertainty (µ must be estimated).&lt;/p&gt;</description></item></channel></rss>