Learning in a medium-scale DSGE model with expectations based on small forecasting models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F12%3A00376792" target="_blank" >RIV/00216208:11640/12:00376792 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1257/mac.4.2.65" target="_blank" >http://dx.doi.org/10.1257/mac.4.2.65</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1257/mac.4.2.65" target="_blank" >10.1257/mac.4.2.65</a>

Result language
angličtina
Original language name
Learning in a medium-scale DSGE model with expectations based on small forecasting models
Original language description
This paper evaluates the empirical performance of a medium-scale DSGE model with agents forming expectations using small forecasting models updated by the Kalman filter. The adaptive learning model fits the data better than the rational expectations (RE)model. Beliefs about the inflation persistence explain the observed decline in the mean and the volatility of inflation as well as Phillips curve flattening. Learning about inflation results in lower estimates for the persistence of the exogenous shocksthat drive price and wage dynamics in the RE version of the model. Expectations based on small forecasting models are closely related to the survey evidence on inflation expectations.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AH - Economics
OECD FORD branch
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Project
<a href="/en/project/GCP402%2F11%2FJ018" target="_blank" >GCP402/11/J018: Comparative Approach to Macroeconomic Modeling and Policy Analysis: Introducing Adaptive Learning</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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