<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sergey Slobodyan | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/sergey-slobodyan/</link><description>Sergey Slobodyan</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/sergey-slobodyan/index.xml" rel="self" type="application/rss+xml"/><item><title>Professional survey forecasts and expectations in DSGE models</title><link>https://macropaperwarehouse.com/papers/professional-survey-forecasts-and-expectations-in-dsge-models/</link><guid>https://macropaperwarehouse.com/papers/professional-survey-forecasts-and-expectations-in-dsge-models/</guid><description>&lt;p&gt;This paper asks whether Survey of Professional Forecasters (SPF) data can be efficiently integrated into medium-scale DSGE models, and whether models with imperfectly rational expectations based on Adaptive Learning (AL) outperform the standard Rational Expectations (RE) hypothesis when survey forecasts are used as observables. The authors work with quarterly US data spanning 1981q2–2019q2, using the Philadelphia Fed Real-Time Data Set (first and second releases) alongside SPF nowcasts for inflation, consumption, investment, and output growth. The SPF nowcast is defined as a prediction formed in the middle of period t+1 for period t+1 given information for period t, making it a suitable proxy for the model-based expectation E_t y_{t+1}.&lt;/p&gt;</description></item></channel></rss>