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Article . 2021 . Peer-reviewed
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SSRN Electronic Journal
Article . 2021 . Peer-reviewed
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Article . 2021 . Peer-reviewed
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Research . 2021
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Research . 2021
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Do Inflation Expectations Improve Model-based Inflation Forecasts?

Authors: Bańbura, Marta; Leiva-León, Danilo; Menz, Jan-Oliver;

Do Inflation Expectations Improve Model-based Inflation Forecasts?

Abstract

Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB’s SPF and Consensus Economics compared to their counterparts that do not. The gains in forecast accuracy from incorporating inflation expectations are typically not large but significant in some periods. Both short- and long-term expectations provide useful information. By contrast, incorporating expectations derived from financial market prices or those of firms and households does not lead to systematic improvements in forecast performance. Individual models we consider are typically better than univariate benchmarks but for the euro area the professional forecasters are more accurate, especially in recent years (not always for the countries). The analysis is undertaken for headline inflation and inflation excluding energy and food and both point and density forecast are evaluated using real-time data vintages over 2001-2019.

Related Organizations
Keywords

BayesianVAR, ddc:330, E37, Phillips curve, Inflation expectations, Bayesian VAR, C53, Inflation, E31, Forecasting

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Top 10%
bronze