爱思英语编者按:国际货币基金组织(英语:International Monetary Fund,简称:IMF)是根据1944年7月在布雷顿森林会议签订的《国际货币基金协定》,于1945年12月27日在华盛顿成立的。

 Free exchange

自由交流
A mean feat
战绩平平
 
Despite forecasters' best efforts, growth is devilishly hard to predict
尽管预测者尽了最大的努力,预测增长是简直比登天还难
 
经济学.jpg
 
“THE only function of economic forecasting is to make astrology look respectable,” John Kenneth Galbraith, an irreverent economist, once said. Since economic output represents the aggregated activity of billions of people, influenced by forces seen and unseen, it is a wonder forecasters ever get it right. Yet economists cannot resist trying. As predictions for 2016 are unveiled, it is worth assessing the soothsayers’ records.
 
“经济方面的预测的唯一功能就是让占星术看上去令人令人起敬,”不留情面的经济学家约翰•肯尼斯•加尔布雷斯曾经如此说道。由于经济产出代表的是数十亿人的总计行为,受到了看得见的和看不见的力量的影响。因而,预测者预测对了堪称奇迹。然而,经济学家还是禁不住要去试试。随着针对2016年的各种预测纷纷出炉,评估一下预测者的既往战绩是值得的。
 
Forecasters usually rely on two different predictive approaches. One is theory-based, shaped by how economists believe economies behave. The other is data-based, shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. It posits that poorer countries should generally invest more and grow faster than rich ones. Central banks and other big economic institutions use far more complicated formulas, often grouped under the bewildering label of “dynamic stochastic general equilibrium” (DSGE) models. These try to anticipate the ups and downs of big economies by modelling the behaviour of individual households and firms.
 
预测者一般依靠两种不同的预测方法。一种以理论为基础,由经济学家对于经济体表现的信任程度所形成;另一种以数据为基础,由经济体的过去具体表现所形成。理论派最简单的工具是以诺贝尔经济学奖得主罗伯特•索洛的名字命名的索洛增长模型。这种模型假设,较为贫穷的国家通常应当比富裕国家投资得更多、增长得更快。央行和其他大型经济机构使用的是远为复杂的公式,即那些经常被归在“ 动态随机一般均衡”(DSGE)这类令人眼花缭乱的标签之下的模型。它们试图通过为单独的家庭和企业建立模型的方式来预测大型经济体的起起落落。
 
The empirical approach is older; indeed, it was the workhorse of government forecasting in the 1940s and 1950s. Data-based models analyse the relationship between hundreds or thousands of economic variables, from the price of potatoes to snowfall in January. They then work out how zinc sales, for example, affect investment and growth in the years that follow.
 
实证的方法更为久远一些;实际上,它曾是上世纪40年代和50年代政府预测的主力军。基于数据的模型分析的是从土豆价格到1月份降雪等成千上万个经济变量之间关系。之后,它们力争找出诸如锌的销量对于来年投资和增长的具体影响。
 
Both strategies have faced withering criticism. DSGE models, for all their complexity, are typically built around oversimplifications of how markets function and people behave. Data-based models suffer from their own shortcomings. In a paper published in 1995 Greg Mankiw of Harvard University argued that they face insurmountable statistical problems. Too many things tend to happen at once to isolate cause and effect: liberalised trade might boost growth, or liberalisation might be the sort of thing that governments do when growth is rising, or both liberalisation and growth might follow from some third factor. And there are too many potential influences on growth for economists to know whether a seemingly strong relationship between variables is real or would disappear if they factored in some other relevant titbit, such as the wages of Canadian lumberjacks.
 
这两种方法一直都面临着令人难堪的批评。DSGE模型,尽管非常复杂,但通常都是围绕着有关市场如何发挥作用以及人们如何表现的过度简单化的观点建立起来的。基于数据的模型则深受各种自身缺陷之苦。哈佛大学的格雷格•曼昆曾在发表于1995年的一篇论文中指出,它们都面临着数不胜数的统计方面的问题。太多的事情往往是同时发生,以至于难以区分原因于结果:自由化的贸易可能刺激了增长,或者是自由化可能正是政府在增长处于上升之中时所做的事情,或者是自由化和增长可能来自第三种因素。同时,太多的对于增长的潜在影响也会让经济学家搞不清楚各种变量之间一种看似强大的关系到底是真实的,还是会在加入一些其他相关因素——如加拿大伐木工人的工资——时消失。
 
In practice, most forecasters combine the two approaches and inject, when necessary, a dose of common sense. The IMF, for instance, relies on a global model, built in part on economic theory and in part on data analysis. The global projections generated by that hybrid model are combined with country-specific details to produce country-level forecasts. The country forecasts are then checked for consistency against the global projections and adjusted when necessary—to make sure, for example, that most countries do not show strong trade growth when the global projection heralds a decline in trade. A recent analysis of the IMF's forecasts by the organisation's Independent Evaluation Office concluded that their accuracy was “comparable to that of private-sector forecasts”. But how accurate is that?
 
在实践中,大多数预测者都是把这两种方法结合起来,而且会在必要的时候加入某些共识。例如,国际货币基金组织(IMF)所依赖的就是一种部分建立在经济理论之上,部分建立在数据分析之上的全球模型。由这种混合模型所产生的全球预测与相关国家的具体情况相结合,以产生国家层面的预测。然后,这些国家层面的预测会与全球预测放在一起,接受一致性校验,并且会在必要的时候被加以调整——例如,以便确认,当全球预测预示一场贸易衰落即将来临时,大多数国家没有显示出强劲的贸易增长。由该组织的独立评估办公室作出的国际货币组织的一份最新的分析的结论显示,他们的准确性“堪比私营部门的预测”。但是,它们到底有多准呢?
 
Not very, Lant Pritchett and Larry Summers of Harvard University argued in 2014. Forecasters overestimate the extent to which the future will look like the recent past, they reckon. It is assumed that fast-growing countries will keep speeding along while the economic tortoises continue crawling. The IMF, for instance, reckons that China's GDP growth will decline gently to 6% a year by around 2017, and then accelerate slightly. That is highly unlikely, say Messrs Pritchett and Summers: “Regression to the mean is perhaps the single most robust and empirically relevant fact about cross-national growth rates.” In other words, booming countries slow down and slumping ones speed up.
 
哈佛大学的兰特•普里切特(Lant Pritchett )和拉里•萨默斯(Larry Summers)曾在2004年指出,他们的预测不是很准。他们认为,预测者通常会高估未来之于最近的过去的相似度。一般认为,正在快速增长的国家会保持高速前进,而经济上的乌龟将继续目前的龟步。例如,IMF认为,中国的GDP增速,到2017年,会温和地降至年均6%,之后会轻微地加速。普里切特和萨默斯说,这是非常不可能的:“向均值回归可能是唯一最强有力且最具实证性的有关跨国增长率的相关因素。”也就是说,正在高歌猛进的国家会慢下来,而正处于动荡之中的国家会加速上行。
 
The IMF publishes forecasts for 189 countries twice a year, in April and October, for the year in question and the following one. The Economist has conducted an analysis of them from 1999 to 2014, and compared their accuracy with several slightly less sophisticated forecasting methods: predicting that a country will grow at the same pace as the year before, guessing 4% (which is the average growth rate across all countries during the period) and picking a random number from -2% to 10%. For each method, the absolute difference between the actual and predicted growth rates is calculated and then averaged. The lowest average is taken to be the best performance.
 
国际货币基金组织每年为189个国家做两次预测。一次是在4月,预测的是当年;一次是在10月,预测的是来年。本报对他们在1999年到2014年间的预测做了一次分析,并把这些预测的准确性与几种稍微有点不太成熟的预测方式——一种是预测一个国家将以前一年同样的速度增长,一种是猜测增速是4%(这是所有国家在这期间的平均增长率),一种是从-2%到10%之间选出一个随机数字——做了比较。对于每一种方法,实际和预期增长率之间的绝对差被被算出来,然后再加以平均。最低的平均值会被当作是最佳表现。
 
Encouragingly, the guesses produced by our random-number generator performed worst (see chart); it yielded predictions that were off by 4.4 percentage points on average. Predicting the previous year's growth rate came last-but-one, as Messrs Pritchett and Summers might have foreseen. The projections the IMF made in October of the year being forecast, which were off by an average of 1.5 percentage points, unsurprisingly did best; by that point plenty of actual economic data are available. Yet the quality of the IMF's forecasts deteriorates surprisingly quickly the further from the end of the year in question they are made. Those from April of the preceding year are only slightly more accurate than those generated using the average growth rate.
 
令人振奋的是,由我们的随机数字产生器产生的猜测的表现是最差的(见图表);它产生了与平均值相差4.4个百分点的预测。正如普里切特和萨默斯可能已经预见到的那样,以前一年的增长率所做出的预测排在倒数第二位。不出所料,国际货币基金组织在10月对当年所作出的预测表现得最好,它与平均值相差1.5个百分点;据此,足够的实体经济数据还是管用的。然而,令人吃惊的是,国际货币基金组织的预测的质量迅速地下滑,离着他们在当年年底所作出的预测的质量越来越远。来自前一年4月的预测只比使用平均增长率所产生的预测稍微准确那么一点点。
 
No one expects the Spanish recession
无人预测到西班牙的衰退
 
An important caveat is in order. Forecasts of all sorts are especially bad at predicting downturns. Over the period, there were 220 instances in which an economy grew in one year before shrinking in the next. In its April forecasts the IMF never once foresaw the contraction looming in the next year. Even in October of the year in question, the IMF predicted that a recession had begun only half the time. To be fair, an average-growth prediction also misses 100% of recessions. One model does better, though. Our random-number generator correctly forecast the start of a recession 18% of the time.
 
这就引出了一个重要的警示。所有类型预测在预测衰退方面都表现的特别差。在这段时期内,共有22个经济体在第二年萎缩之前的一年实现了增长的例子。国际货币基金组织从未在其4月的预测中预见到正在渐行渐近的来年的收缩。即便是在当年10月的预测中,国际货币基金组织只在一半的时间中预测到一场危机已经开始。公平地说,平均增速的预测也是100%错过了衰退。然而,有一种模型要做得更好一些。我们的随机数字产生器,在18%的时间中,正确地预测到了一场危机的开始。