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Opinion

NYU’s Tom Sargent Awarded Nobel Prize in Economics

By David Backus, Heinz Riehl Professor of Finance and Economics & Chair of the Department of Economics

david backus opinion article

The most important thing they did was develop language and tools for thinking about dynamic macroeconomic models and linking them to data. In some ways, this line of work took tools from applied mathematics and applied them to economics, but the change in perspective had a huge influence on macroeconomics.

NYU’s Tom Sargent, the William R. Berkley Professor of Economics and Business, a joint appointment between NYU’s Faculty of Arts & Science and the Stern School of Business,
was awarded the “Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel” this morning, together with Chris Sims of Princeton. Sargent and Sims were part of an unusually strong group of young macroeconomists at the University of Minnesota in the 1970s, which included Ed Prescott (2004 Nobelist) and Neil Wallace (perhaps the world’s leading monetary theorist). They worked separately, for the most part, but it’s easy to see influences among them. For decades their students have continued “the Minnesota tradition” all over the world, including NYU and Princeton. Chicago’s Lars Hansen is the most prominent of their students, with enormously influential work on the interface of macroeconomics and finance. No one would have been surprised if he’d been included in this prize.

So what did they do? Their work was aimed at specialists, but let me give it a try. The most important thing they did was develop language and tools for thinking about dynamic macroeconomic models and linking them to data. In some ways, this line of work took tools from applied mathematics and applied them to economics, but the change in perspective had a huge influence on macroeconomics. If you start (as many still do) with some kind of supply and demand diagram, then prices and quantities depend on where the curves intersect (supply equals demand!). But in dynamic settings, your decision today depends on what you expect to happen in the future. Should I buy a computer today or wait for the price to fall? That brings in expectations in a central way: what you expect to happen in the future affects what you do today. The same logic applies to practical issues like monetary and tax policy. The Fed recently announced not only its current policy but its intentions for future policy precisely to make the future clearer to firms and workers. If only we had the same certainty about future US tax policy!

The next question is how to link dynamic models to data. There’s a fundamental issue in macroeconomics about identifying causality. If (say) the Fed raises the interest rate and economic growth slows down, do we have cause and effect? Or would the economy have slowed down anyway? It’s a fundamentally difficult problem because we don’t have a controlled experiment: we can’t run history over again with different Fed policy. In some of their work, they illustrated the difficulties -- in some cases coming close to saying the problem is impossible. In others, they outlined constructive attacks: using information about timing, apply enough economic structure to make causality clear, develop models with minimal structure and see what happens. It’s technical work, but part of everyone’s toolkit today.

Another line of work concerns the difficulty of nailing down expectations. Tom and Chris’s initial work was based on “rational expectations”: that people would use all the available information effectively in forecasting the future. But what if people aren’t sure what’s going on? Or can’t figure out how to use the information they have? Their work is filled with interesting ideas for how to handle these situations, too. One idea is learning: people know something about the world but need to learn the rest, and while they’re learning they may make lots of mistakes. Some of Tim Cogley’s research fits into this category. Another idea is that people can process limited amounts of information at a time. Laura Veldkamp’s work is a good example. A third is that people may choose to protect themselves against things they don’t know: that they face “ambiguous” situations and do their best to avoid the worst-case scenarios. These lines of research are at the forefront of modern economics, something of a rarity when prizes are awarded for work done decades ago.

Press conference details and more information on Professor Thomas Sargent