Learning in an estimated medium-scale DSGE model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F12%3A00373840" target="_blank" >RIV/00216208:11640/12:00373840 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jedc.2011.01.016" target="_blank" >http://dx.doi.org/10.1016/j.jedc.2011.01.016</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jedc.2011.01.016" target="_blank" >10.1016/j.jedc.2011.01.016</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning in an estimated medium-scale DSGE model

Popis výsledku v původním jazyce

We evaluate the empirical relevance of learning by private agents in an estimated medium-scale DSGE model.We replace the standard rational expectations assumption in the Smets and Wouters (2007) model by a constant gain learning mechanism. If agents knowthe correct structure of the model and only learn about the parameters, both expectation mechanisms produce very similar results, and only the transition dynamics that are generated by specific initial beliefs seem to improve the fit. If, instead, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and, depending on the specification of the initial beliefs, the marginal likelihood of the model can improve significantly.These best-fitting models add additional persistence to the dynamics and this reduces the gap between the IRFs of the DSGE model and the more data-driven DSGE-VAR model. However, the learning dynamics do not systematically alter the estimated structural para

Název v anglickém jazyce

Learning in an estimated medium-scale DSGE model

Popis výsledku anglicky

We evaluate the empirical relevance of learning by private agents in an estimated medium-scale DSGE model.We replace the standard rational expectations assumption in the Smets and Wouters (2007) model by a constant gain learning mechanism. If agents knowthe correct structure of the model and only learn about the parameters, both expectation mechanisms produce very similar results, and only the transition dynamics that are generated by specific initial beliefs seem to improve the fit. If, instead, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and, depending on the specification of the initial beliefs, the marginal likelihood of the model can improve significantly.These best-fitting models add additional persistence to the dynamics and this reduces the gap between the IRFs of the DSGE model and the more data-driven DSGE-VAR model. However, the learning dynamics do not systematically alter the estimated structural para

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

<a href="/cs/project/GCP402%2F11%2FJ018" target="_blank" >GCP402/11/J018: Komparativní přístup k makroekonomickému modelování a analýze politiky: uvedení procesu adaptivního učení</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Journal of Economic Dynamics & Control

ISSN

0165-1889

e-ISSN

—

Svazek periodika

36

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

21

Strana od-do

26-46

Kód UT WoS článku

000298571200002

EID výsledku v databázi Scopus

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