Maybe some of the GMU professors spend too much time blogging (as opposed to producing fundamental and applied research), and maybe some of them spend too much time putting up the show. They should focus on improving the standing of their university, first.
If Deep Throat is right and the CFTC has indeed already given its stamp of approval to presidential prediction markets, then HedgeStreet and USFE would be well advised to listen to professor Robin Hanson’-s idea with great attention:
Using data from a site like Tradesports.com to forecast who will win an election is just scratching the surface, said Robin Hanson, associate professor of economics at George Mason University in Fairfax, VA, and one of the founders of the field of prediction markets. Although the economic incentive is high for picking a winner, Hanson would like to see prediction markets forecast the consequences of a candidate getting into office. Will unemployment go up or down? Will we have more or less trouble in Iraq? Will we decrease or increase the deficit? “-The social value of telling people who’-s likely to win is questionable. The social value of telling people the consequences is arguably far higher,”- said Hanson.
My Question To Professor Robin Hanson: The prediction market that would be interesting would be the one featuring the elected candidate (the so-called “-President-Elect”-). But the expiry of the other prediction markets, featuring the defeated presidential candidates, would be impossible to judge, since these presidential candidates by definition won’-t take office and have any power on the US government. And if the game is murky, you won’-t find any traders willing to risk his/her shirt on those kinds of US presidential prediction markets.
Addendum: Robin Hanson has posted a comment, and I republish it here for everyone to see…-
Let U = the unemployment rate, D = Democrats win, and R = Republicans win. An exchange rate between “Pays $U if D” and “Pays $1 if D” gives an estimate of E[U|D]. Similarly, an exchange rate between “Pays $U if R” and “Pays $1 if R” gives an estimate of E[U|R]. We can compare E[U|D] and E[U|R] to see which candidate is expected to have a lower unemployment rate. And we know how to pay off all of these assets, no matter what happens.
Robin Hanson would like to see prediction markets forecast the consequences of a candidate getting into office. – REDUX