Wolfers & Zitzewitz (2006)

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Credibility Rating

4/5

High(4)

High quality. Established institution or organization with editorial oversight and accountability.

Rating inherited from publication venue: NBER

Relevant to AI safety insofar as prediction markets and information aggregation mechanisms are proposed tools for forecasting AI risks, timelines, and governing collective decisions about AI development.

Metadata

Importance: 45/100working paperprimary source

Summary

This NBER working paper by Justin Wolfers and Eric Zitzewitz examines prediction markets as mechanisms for aggregating dispersed information into accurate forecasts. It analyzes the theoretical foundations and empirical performance of prediction markets, exploring their design and potential applications for improving collective forecasting accuracy.

Key Points

•Prediction markets aggregate dispersed private information into probability estimates through price signals, often outperforming expert forecasts.
•The paper examines market microstructure and incentive design issues that affect the accuracy and reliability of prediction market outcomes.
•Explores applications across political, economic, and scientific forecasting domains where aggregating information is valuable.
•Discusses limitations including thin markets, manipulation risks, and legal/regulatory constraints on prediction market operation.
•Provides theoretical grounding for why market-based aggregation can be superior to polls, surveys, or expert panels.

Cited by 1 page

Page	Type	Quality
Prediction Markets (AI Forecasting)	Approach	56.0

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Pegged Exchange Rate Regimes -- A Trap? | NBER 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
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 Pegged Exchange Rate Regimes -- A Trap? 

 
 
 
 Joshua Aizenman 
 
 & Reuven Glick 
 

 
 
 
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 Working Paper 11652
 

 
 DOI 10.3386/w11652
 

 
 Issue Date October 2005 

 

 
 Revision Date December 2006 

 

 
 
 
 
 
This paper studies the empirical and theoretical association between the duration of a pegged exchange rate and the cost experienced upon exiting the regime. We confirm empirically that exits from pegged exchange rate regimes during the past two decades have often been accompanied by crises, the cost of which increases with the duration of the peg before the crisis. We explain these observations in a framework in which the exchange rate peg is used as a commitment mechanism to achieve inflation stability, but multiple equilibria are possible. We show that there are ex ante large gains from choosing a more conservative not only in order to mitigate the inflation bias from the well-known time inconsistency problem, but also to steer the economy away from the high inflation equilibria. These gains, however, come at a cost in the form of the monetary authority's lesser responsiveness to output shocks. In these circumstances, using a pegged exchange rate as an anti-inflation commitment device can create a "trap" whereby the regime initially confers gains in anti-inflation credibility, but ultimately results in an exit occasioned by a big enough adverse real shock that creates large welfare losses to the economy. We also show that the more conservative is the regime in place and the larger is the cost of regime change, the longer will be the average spell of the fixed exchange rate regime, and the greater the output contraction at the time of a regime change.

 
 
 
 

 

 

 
 
 
 
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 Joshua Aizenman and Reuven Glick, "Pegged Exchange Rate Regimes -- A Trap?," NBER Working Paper 11652 (2005), https://doi.org/10.3386/w11652.
 

 
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 September 28, 2005 

 

 

 

 
 
 

 
 

 
 
 
 Published Versions

 Joshua Aizenman & Reuven Glick, 2008. " Pegged Exchange Rate Regimes-A Trap?, " Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(4), pages 817-835, 06. citation courtesy of 

 
 

 
 

 
 Related

 
 
 
 
 Topics

 
 International Economics 
 Trade 
 International Finance 
 International Macroeconomics 
 
 

 
 
 Programs

 
 International Finance and Macroeconomics 
 
 

 
 
 
 
 
 
 
 
 
 

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Resource ID: f77fdd2d7aeb8de2 | Stable ID: sid_rn9NYG1o4r