Survey expectations, learning and inflation dynamics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F25%3A00639779" target="_blank" >RIV/67985998:_____/25:00639779 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11640/25:00646392

Výsledek na webu

<a href="https://doi.org/10.1016/j.euroecorev.2025.105118" target="_blank" >https://doi.org/10.1016/j.euroecorev.2025.105118</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.euroecorev.2025.105118" target="_blank" >10.1016/j.euroecorev.2025.105118</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Survey expectations, learning and inflation dynamics

Popis výsledku v původním jazyce

We propose a framework that exploits survey data on inflation expectations to refine the identification of processes that drive inflation in DSGE models. By decomposing fundamental markup shocks into persistent and transitory components, our approach effectively integrates timely survey information about the nature of inflation shocks, enhancing forecasts of inflation and other macroeconomic variables. Models with expectations based on a learning setup can more effectively utilize signals from the combined datasets of realized inflation and survey forecasts compared to their Rational Expectations counterparts. The learning model’s ability to generate time variation in the perceived inflation target, inflation persistence, and sensitivity to various shocks enables it to detect changes in the fundamental processes driving inflation. These features help overcome limitations of survey data and enhance forecast accuracy, particularly during periods when survey forecasts exhibit systematic prediction errors. Specifically, the model with learning successfully identifies the more persistent nature of the recent inflation surge.

Název v anglickém jazyce

Survey expectations, learning and inflation dynamics

Popis výsledku anglicky

We propose a framework that exploits survey data on inflation expectations to refine the identification of processes that drive inflation in DSGE models. By decomposing fundamental markup shocks into persistent and transitory components, our approach effectively integrates timely survey information about the nature of inflation shocks, enhancing forecasts of inflation and other macroeconomic variables. Models with expectations based on a learning setup can more effectively utilize signals from the combined datasets of realized inflation and survey forecasts compared to their Rational Expectations counterparts. The learning model’s ability to generate time variation in the perceived inflation target, inflation persistence, and sensitivity to various shocks enables it to detect changes in the fundamental processes driving inflation. These features help overcome limitations of survey data and enhance forecast accuracy, particularly during periods when survey forecasts exhibit systematic prediction errors. Specifically, the model with learning successfully identifies the more persistent nature of the recent inflation surge.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/LX22NPO5101" target="_blank" >LX22NPO5101: Národní institut pro výzkum socioekonomických dopadů nemocí a systémových rizik</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

European Economic Review

ISSN

0014-2921

e-ISSN

1873-572X

Svazek periodika

180

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

35

Strana od-do

105118

Kód UT WoS článku

001590412900001

EID výsledku v databázi Scopus

2-s2.0-105017613148