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Economics Nobelists Tackle Time
8 October 2003 (All day)
Time is not always on your side, at least in the volatile world of finance. The complicated mathematics of analyzing the way data sets behave over time long stumped economists, but Robert Engle and Clive Granger have helped clear up the confusion. The two have won this year's Bank of Sweden Prize in Economics for giving analysts tools to understand time series in economics.
A common example of a time series is a stock index, in which value falls and rises over time. To the chagrin of many a stockholder, time series are often hard to predict and analyze. Economists Engle, of New York University, and Granger, of the University of California, San Diego, revolutionized the way the financial community dissects these unpredictable series.
Granger showed that simplistic assumptions about the time-dependent behavior of economic indicators can result in ridiculous conclusions; for example, two random processes can appear to be linked even though they're independent. He also came up with ways of measuring whether two indicators that meander in seemingly different ways have hidden relationships, and of determining when one economic effect causes another. "This has serious implications for economic policy," says Princeton University economist Yacine Aït-Sahalia. "You want to know, for example, do changes in interest rates cause a change in employment?"
Engle is best known for developing a mathematical technique for analyzing time series in which the variance, or volatility, of an indicator can change over time. According to Princeton's Christopher Sims, this ability is vital. "In finance, you want to predict how volatility changes because you can make money off of that," he says. "Variance is absolutely central to evaluating risk."
Engle and Granger will split the $1.3 million prize and attend the ceremony in Stockholm in December. In the meantime, financiers will keep trying to use the pair's methods to make money, and economists will continue to use their analyses and methods to make crucial forecasts. "I think the beauty of it is that the stuff just works," says Aït-Sahalia.