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Survey expectations, learning and inflation dynamics

The result's identifiers

  • Result code in 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>

  • Alternative codes found

    RIV/00216208:11640/25:00646392

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Survey expectations, learning and inflation dynamics

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50202 - Applied Economics, Econometrics

Result continuities

  • Project

    <a href="/en/project/LX22NPO5101" target="_blank" >LX22NPO5101: The National Institute for Research on the Socioeconomic Impact of Diseases and Systemic Risks</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2025

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    European Economic Review

  • ISSN

    0014-2921

  • e-ISSN

    1873-572X

  • Volume of the periodical

    180

  • Issue of the periodical within the volume

    November

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    35

  • Pages from-to

    105118

  • UT code for WoS article

    001590412900001

  • EID of the result in the Scopus database

    2-s2.0-105017613148