Survey expectations and learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F21%3A00544621" target="_blank" >RIV/00216208:11640/21:00544621 - isvavai.cz</a>

Výsledek na webu

<a href="https://rjmf.econs.online/upload/iblock/646/Survey_Expectations_and_Learning.pdf" target="_blank" >https://rjmf.econs.online/upload/iblock/646/Survey_Expectations_and_Learning.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31477/rjmf.202102.03" target="_blank" >10.31477/rjmf.202102.03</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Survey expectations and learning

Popis výsledku v původním jazyce

In this paper, we evaluate a model that describes real-time inflation data together with the inflation expectations measured by the Survey of Professional Forecasters (SPF). We work with a second-order autoregressive model in which the agents learn over time the intercept and persistence coefficients based on real-time data. To model the process of revisions in real time data, we allow for news and noise disturbances. In contrast to the usual time-varying parameter vector autoregression, we use non-linear Kalman filter techniques to estimate the time-varying coefficients of the underlying inflation process. We identify systematic changes in the persistence of the inflation process and in the long-run expected inflation rate that are implied by the model. The inflation forecasts implied by the model are then compared with the SPF forecasts. As we cannot reject the hypothesis that the SPF forecasts are produced based on our model, we re-estimate the model using Survey nowcasts and forecasts as additional observables. This augmented model does not change the nature and magnitude of the time variation in the coefficients of the autoregressive model, but it does help to reduce the uncertainty in the estimates. Overall, the estimated time-variation confirms our results on the perceived inflation process present in estimated DSGE models with learning.

Název v anglickém jazyce

Survey expectations and learning

Popis výsledku anglicky

In this paper, we evaluate a model that describes real-time inflation data together with the inflation expectations measured by the Survey of Professional Forecasters (SPF). We work with a second-order autoregressive model in which the agents learn over time the intercept and persistence coefficients based on real-time data. To model the process of revisions in real time data, we allow for news and noise disturbances. In contrast to the usual time-varying parameter vector autoregression, we use non-linear Kalman filter techniques to estimate the time-varying coefficients of the underlying inflation process. We identify systematic changes in the persistence of the inflation process and in the long-run expected inflation rate that are implied by the model. The inflation forecasts implied by the model are then compared with the SPF forecasts. As we cannot reject the hypothesis that the SPF forecasts are produced based on our model, we re-estimate the model using Survey nowcasts and forecasts as additional observables. This augmented model does not change the nature and magnitude of the time variation in the coefficients of the autoregressive model, but it does help to reduce the uncertainty in the estimates. Overall, the estimated time-variation confirms our results on the perceived inflation process present in estimated DSGE models with learning.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

50202 - Applied Economics, Econometrics

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

2021

Kód důvěrnosti údajů

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

Název periodika

Russian Journal of Money and Finance

ISSN

0130-3090

e-ISSN

—

Svazek periodika

80

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

RU - Ruská federace

Počet stran výsledku

25

Strana od-do

3-27

Kód UT WoS článku

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EID výsledku v databázi Scopus

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