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How prediction markets can save event studies

Author: Erik Snowberg; Justin Wolfers; Eric Zitzewitz; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, ©2011.
Series: Working paper series (National Bureau of Economic Research), no. 16949.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions from the economic effects of party control of government to the importance of complex rules in congressional committees. However, the results of event studies are notoriously  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Erik Snowberg; Justin Wolfers; Eric Zitzewitz; National Bureau of Economic Research.
OCLC Number: 713565203
Notes: "April 2011."
Title from http://www.nber.org/papers/16949 viewed Apr. 20, 2011.
Description: 1 online resource (13, 3, 8 p.) : ill.
Series Title: Working paper series (National Bureau of Economic Research), no. 16949.
Responsibility: Erik Snowberg, Justin Wolfers, Eric Zitzewitz.

Abstract:

This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions from the economic effects of party control of government to the importance of complex rules in congressional committees. However, the results of event studies are notoriously sensitive to both choices made by researchers and external events. Specifically, event studies will generally produce different results depending on three interrelated things: which event window is chosen, the prior probability assigned to an event at the beginning of the event window, and the presence or absence of other events during the event window. In this paper we show how each of these may bias the results of event studies, and how prediction markets can mitigate these biases.

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