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Bertrand Gruss, Mrs. Sandra V Lizarazo Ruiz, and Mr. Francesco Grigoli

decisions lead to a significant increase in the dispersion of inflation expectations for horizons up to nine months after the policy rate decision is taken. The effect of monetary surprises on dispersion of inflation expectations at longer horizons is not statically significant. Then, to rationalize the empirical results, we propose a partial equilibrium rational expectations model with sticky information, in the spirit of Mankiw and Reis (2002) . We contend that information rigidity is essential to explain the empirical patterns. To make our point, we show that the

Bertrand Gruss, Mrs. Sandra V Lizarazo Ruiz, and Mr. 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.
Jonas Dovern, Mr. Ulrich Fritsche, Mr. Prakash Loungani, and Ms. Natalia T. Tamirisa
We study forecasts for real GDP growth using a large panel of individual forecasts from 36 advanced and emerging economies during 1989–2010. We show that the degree of information rigidity in average forecasts is substantially higher than that in individual forecasts. Individual level forecasts are updated quite frequently, a behavior more in line “noisy” information models (Woodford, 2002; Sims, 2003) than with the assumptions of the sticky information model (Mankiw and Reis, 2002). While there are cross-country variations in information rigidity, there is no systematic difference between advanced and emerging economies.
Jonas Dovern, Mr. Ulrich Fritsche, Mr. Prakash Loungani, and Ms. Natalia T. Tamirisa

information rigidities, that is, departures from full information rational expectations. In an important set of papers, Coibion and Gorodnichenko ( 2010 , 2012 ) showed that canonical versions of both theories—dubbed respectively as the ‘sticky information’ and ‘imperfect information’ models—predict that average forecast errors should be correlated with the past forecast revision. In this paper, we first show that there is an equivalent way to document information rigidities, which is to look at the correlation between the current forecast revision and the past forecast

Jonas Dovern, Mr. Ulrich Fritsche, Mr. Prakash Loungani, and Ms. Natalia T. Tamirisa
We examine the behavior of forecasts for real GDP growth using a large panel of individual forecasts from 30 advanced and emerging economies during 1989–2010. Our main findings are as follows. First, our evidence does not support the validity of the sticky information model (Mankiw and Reis, 2002) for describing the dynamics of professional growth forecasts. Instead, the empirical evidence is more in line with implications of "noisy" information models (Woodford, 2002; Sims, 2003). Second, we find that information rigidities are more pronounced in emerging economies than advanced economies. Third, there is evidence of nonlinearities in forecast smoothing. It is less pronounced in the tails of the distribution of individual forecast revisions than in the central part of the distribution.
Jonas Dovern, Mr. Ulrich Fritsche, Mr. Prakash Loungani, and Ms. Natalia T. Tamirisa

Gorodnichenko ( 2010 , 2012 ) showed that canonical versions of both classes of models—dubbed respectively as the ‘sticky information’ and ‘imperfect information’ models—have the feature that the forecast error should be correlated with the forecast revision, which is the first of the two tests proposed by Nordhaus. A third class of theories suggests behavioral explanations for forecast rigidity, and these are mentioned by Nordhaus as an explanation for his findings. Citing Tversky and Kahneman (1981) , he states that “we tend to break the good or bad news to ourselves

Mr. Olivier Coibion and Mr. Yuriy Gorodnichenko

Front Matter Page Research Department Contents I. Introduction II. Forecast Errors, Forecast Revisions and Information Rigidities A. Sticky-Information Model B. Noisy-Information Model C. A New Approach for Assessing the Nature of the Expectations Formation Process D. Extensions and Alternative Interpretations Heterogeneity in Signal-Noise Ratios Forecast Smoothing III. Information Rigidities across Agent Types, Macroeconomic Variables, and Countries A. Information Rigidity across Agents B. Information Rigidity across