Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts

By Olivier Coibion, Y. Gorodnichenko. Social Science Research Network, 2012

1,131 citations recorded by the source corpus.

We propose a new approach to test of the null of full-information rational expectations which is informative about whether rejections of the null reflect departures from rationality or full-information. This approach can also quantify the economic significance of departures from the null by mapping them into the underlying degree of information rigidity faced by economic agents. Applying this approach to both U.S. and cross-country data of professional forecasters and other economic agents yields pervasive evidence of informational rigidities that can be explained by models of imperfect information. Furthermore, the proposed approach sheds new light on the implications of policies such as inflation-targeting and those leading to the Great Moderation on expectations. Finally, we document evidence of state-dependence in the expectations formation process.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/306bebc76dfbde1549cbcbb0f9447b7c6c25dba8

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Citations in the corpus

Firm performance and macro forecast accuracy (2018, neutral): “Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 3,130 3,344 6,671 2,891 3,201 6,628 3,130 3,345 6,671 N firms 504 588 1,063 482 579 1,054 504 589 1,063 (10) (11) (12) (13) (14) (15) Profit Profit Profit TFP TFP TFP Export status Non-exporter Non-e”
News and uncertainty about COVID-19: Survey evidence and short-run economic impact (2020, positive): “There is, however, evidence that expectations adjust more quickly in times of high uncertainty, in response to large shocks, and as media coverage intensiﬁes ( Baker et al., 2020b; Coibion and Gorodnichenko, 2015; Larsen et al., 2021 ).”
Testing the Sticky Information Phillips Curve (2010, background): “is not the case for the NKPC, for which the empirical fit is 27 I am grateful to an anonymous referee for this suggestion. 28 See Coibion (2006). 29 Woodford (2003) argues that plausible values of are between 0.”
Overreaction in Macroeconomic Expectations (2018, positive): “Conclusion Using data from the Blue Chip Survey and from the Survey of Professional Forecasters, we study how professional forecasters react to news building on the methodology of Coibion and Gorodnichenko (2015).”
Inattentive professional forecasters (2013, neutral): “10See, for example, Andrade & Gregoir (2010) and Coibion & Gorodnichenko (2010) for recent references.”
Diagnostic Expectations and Stock Returns (2019, background): “To assess overreaction, we need a measure of news at each t. Coibion and Gorodnichenko (2015) propose analysts’ forecast revision at t, which summarizes all information received by the forecaster in this period.”
Why are Target Interest Rate Changes so Persistent (2012, background): “…path of future interest rates over financial market and professional forecasts is strongly suggestive of important informational frictions facing these agents, as found in Coibion and Gorodnichenko (2010) in the case of inflation forecasts from both professional forecasters and financial markets.”
The Price Is Right: Updating Inflation Expectations in a Randomized Price Information Experiment (2016, background): “28 Coibion and Gorodnichenko (2012a) also show how noisy-information and sticky-information models can be unified in a useful framework of information rigidities. 29 This holds for additive i.i.d. noise in private signals and for AR inflation (of any order). See also Andrade and LeBihan (2013) for a”
What Can Survey Forecasts Tell Us about Information Rigidities? (2012, background): “Andrade and Le Bihan (2010) and Coibion and Gorodnichenko (2010) consider sticky information and noisy information in professional forecasts.”