Soft science no more
This year's Nobel prize shows how far number crunchers have come in economics
ONCE, a beautiful turn of phrase would take you a long way in economics. From Adam Smith to John Maynard Keynes, economists were content to put their theories and ideas into (mostly) English prose, and leave it at that. Their big, breezy thoughts made great, but imprecise reading. Contradictions were glossed over. So prolix was Keynes, for example, that he is thought to have said everything at least once.
This will no longer do. Since 1945 or so, practitioners of what was once called “political economy” have become more demanding. They sought to test their grand thoughts against the hard facts of the real world. Incomes, interest rates, and prices of all sorts could be measured. Did they behave as theory supposed? National accounts, the detailed measures of GDP, were just being created, at Keynes's behest. But economic data were, and still are, messy; analysing them even more so. It took decades to develop the tools to detect and measure economic relationships with much certainty.
This year's Nobel prize has gone to two economists who epitomise the rise of statistical techniques: Robert Engle, an American economist at New York University, and Clive Granger, a Briton at the University of California at San Diego. They have crafted some of the most sophisticated tools to analyse economic data. Their contributions, developed during the 1980s, deal especially with “time-series” data: share prices, household consumption, inflation—anything, in fact, that changes over time, and thus poses difficulties for older forms of statistical analysis.
今年的诺贝尔奖花落两位代表统计技术崛起的经济学家：纽约大学经济学家、美国人罗伯特·恩格尔 （Robert Engle）和加州大学圣迭戈分校、英国人克莱夫·格兰杰（Clive Granger）。他们精心打造出了一批高精尖的工具，以分析经济数据。他们在上世纪80年代做出的贡献专门对付“时间序列”（time-series）数据：股票价格、家庭消费、通货膨胀——实际上，凡是随时间而变化、因而给老式统计分析带来困难的东西。
Poets and plumbers
The Nobel committee seems to be highlighting the wide range of ideas and skills that comprise modern economics: a healthy equilibrium, you might say, between poets and plumbers. Last year one of the two winners was a psychologist whose findings contradict many of the assumptions of economic theory. This year the plumbers were back. Only three years have passed since James Heckman and Daniel McFadden were also honoured for sharpening econometrics, the statistical methods with which economic data are analysed.
Mr Engle and Mr Granger have crafted techniques that demand even greater virtuosity at maths, but which are nevertheless crucial in separating wheat from chaff. Mr Engle's work has helped build the foundations for measuring and avoiding myriad types of risks in the modern economy. He has studied the volatility—the severity of swings—of time series ranging from inflation to the prices of securities. Anyone who watches the stockmarkets knows that they undergo periods of wild adolescent swings as well as times of geriatric languor. Until Mr Engle came along, people interested in such things—financial types, mostly, but also regulators—used crude measures of historical volatility, looking back over a year, say, to see what the average of the swings was. They would then use this as a gauge of likely future volatility.
Mr Engle's approach, ARCH (for autoregressive conditional heteroscedasticity, should you insist on knowing) gave researchers the power to test whether and how volatility in one period is related to volatility in earlier times. There often is a link, as casual observation suggests. After several days of stockmarket upheaval, there may be several days of calm. A 3% rise or fall in shares is often heralded by increasing volatility, much as an earthquake is preceded by tremors. Mr Engle's high-powered maths has made market risk easier to forecast. Thus banks and investors who use “value at risk” techniques to analyse their portfolios owe much to Mr Engle. So does the Basel committee which is drawing up new rules for banks' capital requirements.
恩格尔的方法——ARCH（如果非想知道的话，它代表autoregressive conditional heteroscedasticity，即自回归条件异方差性）给了研究者验证一个周期中的波幅是否与先前周期中的波幅相关以及如何相关的力量。如偶尔的观测所示，这里经常存在一种联系。数天的股市上涨后，可能有几天的平静期。股票上涨或下跌3%经常是以日渐加大的波幅为前兆，这同地震以震动为先兆非常相似。恩格尔的高性能数学让市场风险更容易预测。因而，使用“风险估值”（value at risk）技术分析投资组合的银行和投资者亏欠恩格尔许多。正在为银行资本金要求制定新规的巴塞尔委员会也是如此。
Mr Granger's research was aimed more at coming to grips with longer-term swings in economic growth, inflation and currencies than with shorter bouts of risk and volatility. Macroeconomic data often share some common features. GDP per head, for example, has tended to grow over time (at least for as long as it has been reliably measured). But the “trend” rate of growth discussed by forecasters—and, yes, by journalists—is never as fixed as they make it seem: it can be influenced by shocks like rising oil prices or wars.
This may obscure deeper relationships hidden in the data, posing tricky problems for statisticians. Using standard statistical tools—derived from things that do not change much over time, such as the distribution of people's heights—can be misleading. An economist using these tools might conclude that a casual relationship existed where none really does, and thus be fooled by a statistical mirage.
Mr Granger devised a clever solution to this, called co-integration. He made use of the notion of economic equilibrium—the idea that variables tend to move towards particular values, and thus in a predictable direction. He found that when two sets of economic data are compared, for example inflation and exchange rates, they can often be treated with standard techniques. In collaboration with Mr Engle, he worked to create tests for economists to ensure that they were getting reliable results when making such comparisons.
对此，格兰杰设计了一种聪明的解决方案——协整 （ co-integration）。他用到了经济平衡的概念——即各种变量往往是向着特定的价值、因而是在一种可以预测的方向中运动的思想。他发现，当两套经济数据，如通胀和汇率，被拿来对比时，它们可能经常被待之以标准技术。在与恩格尔的合作中，格兰杰曾为了给经济学家创造出能够确保他们在做此类对比时得到可信结果的验证而全力以赴。
Despite these sophisticated techniques, which economists now apply as a matter of course, the analysis of economic data remains messy. “Driving a Mercedes down a cow-track” is how Thomas Mayer, an American academic economist, once described the application of fancy tools to real-world phenomena that are not easy to model, much less to measure.
Even so, the place of econometricians at the centre of economics is now confirmed. Indeed, there now seems to be a dearth of the grand theorists of days past. Specialisation—to use an economists' term—is the order of the day. But then a good plumber is in greater demand than any poet.