information rigidity
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2021 ◽  
Author(s):  
Tucker McElroy ◽  
Xuguang Simon Sheng
Keyword(s):  

2020 ◽  
Vol 20 (252) ◽  
Author(s):  
Bertrand Gruss ◽  
Sandra Lizarazo ◽  
Francesco Grigoli

Anchoring of inflation expectations is of paramount importance for central banks’ ability to deliver stable inflation and minimize price dispersion. Relying on daily interest rates and inflation forecasts from major financial institutions in the United States, we calculate monetary policy surprises of individual analysts as the unexpected changes in the federal funds rate before the meetings of the Federal Reserve Board. We then assess the effect of monetary policy surprises on the dispersion of inflation expectations, a proxy for the extent of anchoring, which is based on the same analysts’ inflation projections submit-ted after the Fed meetings. With an identification strategy that hinges on a tight window around the Fed meetings, we find that monetary policy surprises lead to an increase in the dispersion of inflation expectations up to nine months after the policy meeting. We rationalize these results with a partial equilibrium model that features rational expectations and sticky information. When we allow the degree of information rigidity to depend on the realization of firm-specific shocks, the theoretical results are qualitatively consistent and quantitatively close to the empirical evidence.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zidong An ◽  
Joao Tovar Jalles

PurposeThis paper contributes to shed light on the quality and performance of US fiscal forecasts.Design/methodology/approachThe first part inspects the causes of official fiscal forecasts revisions by Congressional Budget Office (CBO) between 1984 and 2016 that are due to technical, economic or policy reasons.FindingsBoth individual and cumulative means of forecast errors are relatively close to zero, particularly in the case of expenditures. CBO averages indicate net average downward revenue and expenditure revisions and net average upward deficit revisions. Focusing on the causes of the technical component, the authors uncover that its revisions are quite unpredictable, which cast doubts on inferences about fiscal policy sustainability that rely on point estimates. Comparing official with private-sector (consensus) forecasts, despite the informational advantages CBO might have, one cannot unequivocally say that one or the other is more accurate. Evidence also seems to suggest that CBO forecasts are consistently heavily biased toward optimism while this is less the case for consensus forecasts. Not only is the extent of information rigidity is more prevalent in CBO forecasts but also evidence seems to indicate that consensus forecasts dominate CBO in terms of information content.Originality/valueThe authors provide a detailed analysis on US fiscal forecasts both using revenue and expenditure and decomposing forecast errors into several explanatory components. Moreover, the authors compare official with private-sector (consensus) forecasts and assess which one is better or preferred.


2020 ◽  
Vol 102 (2) ◽  
pp. 287-303 ◽  
Author(s):  
Scott R. Baker ◽  
Tucker S. McElroy ◽  
Xuguang S. Sheng

By matching a large database of individual macroforecaster data with the universe of sizable natural disasters across 54 countries, we identify a set of new stylized facts: forecasters are persistently heterogeneous in how often they issue or revise a forecast; information rigidity declines significantly following large, unexpected natural disaster shocks; and disagreement decreases among inattentive agents while it might increase for attentive ones. We develop a learning model that captures the two channels through which natural disaster shocks affect expectation formation: attention effect—the visibly large shocks induce immediate and synchronized updating of information for inattentive agents—and uncertainty effect—attentive agents might increase their acquisition of private information to compensate for the higher uncertainty after shocks.


2018 ◽  
Vol 2018 (335) ◽  
Author(s):  
Akihisa Shibata ◽  
◽  
Mototsugu Shintani ◽  
Takayuki Tsuruga ◽  
◽  
...  

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