Can the Market Set Better Monetary Policy Than the Fed?


For the past year, Hypermind has partnered with the Mercatus Center at George Mason University to try to answer one big question. Here’s how they formulate it on their website:

Currently, US monetary policy is largely based on the Federal Reserve targeting inflation to keep the economy stable. That means it ensures that inflation—the increase in the prices of goods and services—does not venture too far from 2 percent. But for years, inflation has stayed below 2 percent and all the while, we have not seen strong levels of economic growth.

The Mercatus Center’s Scott Sumner and David Beckworth have made the case that an alternative monetary policy approach, nominal gross domestic product (NGDP) level targeting, is superior to inflation targeting. According to Sumner and Beckworth, instead of targeting inflation, the Federal Reserve’s monetary policy should target the rate at which the nation’s total income is expected to grow. NGDP level targeting will ensure that the right amount of money supply is provided to meet the economy’s needs.

But how do you determine if monetary policy is set appropriately to produce stable growth in NGDP? Let’s let the market decide, Sumner and Beckworth hypothesize.

That’s where Hypermind comes in.

We have created a prediction market to forecast NGDP growth over the first two years of Donald Trump’s presidency. It is generously endowed by Mercatus with $70,000 in prizes for those whose predictions are most foresighted. Participation is free of charge and open to everyone interested in the topic, but the prizes are very real! To participate, just sign up at

Here’s what the folks at Mercatus are hoping to find out:

The project’s goal is to determine whether the market can adequately determine the trajectory of NGDP growth and whether the Fed should use this information to inform policymaking and to move towards an NGDP level targeting regime. 

In the meantime, click here to view the latest market forecasts (updated in real time).

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