scholarly journals Overreaction in Macroeconomic Expectations

2020 ◽  
Vol 110 (9) ◽  
pp. 2748-2782 ◽  
Author(s):  
Pedro Bordalo ◽  
Nicola Gennaioli ◽  
Yueran Ma ◽  
Andrei Shleifer
Keyword(s):  
Rational Expectations ◽  
Bayesian Updating ◽  
Learning Model ◽  
Forecast Errors ◽  
Full Information ◽  
Distortion Parameter ◽  
Forecast Revisions ◽  
Financial Variables ◽  
Consensus Forecasts ◽  
Important Variation

We study the rationality of individual and consensus forecasts of macroeconomic and financial variables using the methodology of Coibion and Gorodnichenko (2015), who examine predictability of forecast errors from forecast revisions. We find that individual forecasters typically overreact to news, while consensus forecasts under-react relative to full-information rational expectations. We reconcile these findings within a diagnostic expectations version of a dispersed information learning model. Structural estimation indicates that departures from Bayesian updating in the form of diagnostic overreaction capture important variation in forecast biases across different series, yielding a belief distortion parameter similar to estimates obtained in other settings. (JEL C53, D83, D84, E13, E17, E27, E47)

scholarly journals The Formation of Expectations, Inflation, and the Phillips Curve

2018 ◽  
Vol 56 (4) ◽  
pp. 1447-1491 ◽  
Author(s):  
Olivier Coibion ◽  
Yuriy Gorodnichenko ◽  
Rupal Kamdar
Keyword(s):  
Survey Data ◽  
Rational Expectations ◽  
Phillips Curve ◽  
Rational Expectation ◽  
New Keynesian ◽  
Inflation Expectations ◽  
Full Information ◽  
Macroeconomic Modeling ◽  
Expectations Formation

This paper argues for a careful (re)consideration of the expectations formation process and a more systematic inclusion of real-time expectations through survey data in macroeconomic analyses. While the rational expectations revolution has allowed for great leaps in macroeconomic modeling, the surveyed empirical microevidence appears increasingly at odds with the full-information rational expectation assumption. We explore models of expectation formation that can potentially explain why and how survey data deviate from full-information rational expectations. Using the New Keynesian Phillips curve as an extensive case study, we demonstrate how incorporating survey data on inflation expectations can address a number of otherwise puzzling shortcomings that arise under the assumption of full-information rational expectations. (JEL D04, E24, E27, E31, E37)

scholarly journals Seeking Consensus: A New Approach

2010 ◽  
Vol 138 (12) ◽  
pp. 4402-4415 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Minimum Temperature ◽  
Forecast Accuracy ◽  
Cost Savings ◽  
Absolute Error ◽  
Electricity Production ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Temperature Forecast ◽  
Model Output Statistics ◽  
The Individual

Abstract Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

scholarly journals Coordinating expectations through central bank projections

2020 ◽  
Author(s):  
Fatemeh Mokhtarzadeh ◽  
Luba Petersen
Keyword(s):  
Central Bank ◽  
Rational Expectations ◽  
Inflation Targeting ◽  
Aggregate Stability ◽  
Central Banks ◽  
Output Gap ◽  
Forecast Errors ◽  
Market Participants ◽  
Wide Range ◽  
Ex Ante

AbstractCentral banks are increasingly communicating their economic outlook in an effort to manage the public and financial market participants’ expectations. We provide original causal evidence that the information communicated and the assumptions underlying a central bank’s projection can matter for expectation formation and aggregate stability. Using a between-subject design, we systematically vary the central bank’s projected forecasts in an experimental macroeconomy where subjects are incentivized to forecast the output gap and inflation. Without projections, subjects exhibit a wide range of heuristics, with the modal heuristic involving a significant backward-looking component. Ex-Ante Rational dual projections of the output gap and inflation significantly reduce the number of subjects’ using backward-looking heuristics and nudge expectations in the direction of the rational expectations equilibrium. Ex-Ante Rational interest rate projections are cognitively challenging to employ and have limited effects on the distribution of heuristics. Adaptive dual projections generate unintended inflation volatility by inducing boundedly-rational forecasters to employ the projection and model-consistent forecasters to utilize the projection as a proxy for aggregate expectations. All projections reduce output gap disagreement but increase inflation disagreement. Central bank credibility is significantly diminished when the central bank makes larger forecast errors when communicating a relatively more complex projection. Our findings suggest that inflation-targeting central banks should strategically ignore agents’ irrationalities when constructing their projections and communicate easy-to-process information.

scholarly journals On the performance of US fiscal forecasts: government vs. private information

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zidong An ◽  
Joao Tovar Jalles
Keyword(s):  
Private Sector ◽  
Private Information ◽  
Forecast Errors ◽  
Content Type ◽  
Consensus Forecasts ◽  
Point Estimates ◽  
Congressional Budget Office ◽  
And Performance ◽  
Information Rigidity ◽  
Fiscal Policy Sustainability

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.

scholarly journals The Role of Anchoring Bias in the Equity Market: Evidence from Analysts’ Earnings Forecasts and Stock Returns

2012 ◽  
Vol 48 (1) ◽  
pp. 47-76 ◽  
Author(s):  
Ling Cen ◽  
Gilles Hilary ◽  
K. C. John Wei
Keyword(s):  
Stock Returns ◽  
Earnings Forecasts ◽  
Equity Market ◽  
Stock Splits ◽  
Forecast Errors ◽  
Forecast Revisions ◽  
Earnings Per Share ◽  
Earnings Surprises ◽  
Anchoring Bias

AbstractWe test the implications of anchoring bias associated with forecast earnings per share (FEPS) for forecast errors, earnings surprises, stock returns, and stock splits. We find that analysts make optimistic (pessimistic) forecasts when a firm’s FEPS is lower (higher) than the industry median. Further, firms with FEPS greater (lower) than the industry median experience abnormally high (low) future stock returns, particularly around subsequent earnings announcement dates. These firms are also more likely to engage in stock splits. Finally, split firms experience more positive forecast revisions, more negative forecast errors, and more negative earnings surprises after stock splits.

scholarly journals Performance of Hourly Operational Consensus Forecasts (OCFs) in the Australian Region

2007 ◽  
Vol 22 (6) ◽  
pp. 1345-1359 ◽  
Author(s):  
Chermelle Engel ◽  
Elizabeth Ebert
Keyword(s):  
Wind Speed ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Weather Prediction ◽  
Weighted Average ◽  
Barometric Pressure ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Dewpoint Temperature ◽  
Statistical Correction

Abstract This paper presents an extension of the operational consensus forecast (OCF) method, which performs a statistical correction of model output at sites followed by weighted average consensus on a daily basis. Numerical weather prediction (NWP) model forecasts are received from international centers at various temporal resolutions. As such, in order to extend the OCF methodology to hourly temporal resolution, a method is described that blends multiple models regardless of their temporal resolution. The hourly OCF approach is used to generate forecasts of 2-m air temperature, dewpoint temperature, RH, mean sea level pressure derived from the barometric pressure at the station location (QNH), along with 10-m wind speed and direction for 283 Australian sites. In comparison to a finescale hourly regional model, the hourly OCF process results in reductions in average mean square error of 47% (air temperature), 40% (dewpoint temperature), 43% (RH), 29% (QNH), 42% (wind speed), and 11% (wind direction) during February–March with slightly higher reductions in May. As part of the development of the approach, the systematic and random natures of hourly NWP forecast errors are evaluated and found to vary with forecast hour, with a diurnal modulation overlaying the normal error growth with time. The site-based statistical correction of the model forecasts is found to include simple statistical downscaling. As such, the method is found to be most appropriate for meteorological variables that vary systematically with spatial resolution.

scholarly journals Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting

2020 ◽  
Vol 2020 (089) ◽  
pp. 1-56
Author(s):  
Andrew C. Chang ◽  
◽  
Trace J. Levinson ◽  
Keyword(s):  
Forecast Errors ◽  
Time Varying ◽  
Open Market ◽  
Federal Open Market Committee ◽  
Forecast Revisions ◽  
Consumption Expenditures ◽  
Inflation Forecasts ◽  
Board Of Governors ◽  
Personal Consumption ◽  
Reserve System

We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's GDP forecast errors correlate with its GDP forecast revisions, particularly for forecasts made more than two weeks from the start of a FOMC meeting, implying GDP forecasts exhibit time-varying inefficiency between FOMC meetings. We find some weaker evidence for inefficient inflation forecasts.

The Impact of Analysts' Forecast Errors and Forecast Revisions on Stock Prices

2008 ◽  
Vol 35 (5-6) ◽  
pp. 709-740 ◽  
Author(s):  
William Beaver ◽  
Bradford Cornell ◽  
Wayne R. Landsman ◽  
Stephen R. Stubben
Keyword(s):  
Stock Prices ◽  
Forecast Errors ◽  
Forecast Revisions ◽  
The Impact
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