经济学人

are updated every day, so they can in theory capture shifts in consumer behaviour before official numbers are released.

Varian和谷歌的另一名同事Hyunyoung Choi最新发表了一篇论文，他表示对于人们在网上搜索的一些字词短语，其频率的变化可以提高经济计量模型预测零售销售或售房数据的准确性。这些确切销售数据通常是要滞后一段时间才能得到。但是谷歌搜索数据每天都在更新，因此理论上在官方数字出炉之前，们就把握住了消费者行为的变化。

These data are available through a site called Google Trends, which allows anyone who cares to do so to download an index of the aggregate volume of searches for particular terms or categories. Mr Varian and Mr Choi show that the addition of these search trends to econometric models improves the accuracy of their estimates.

人们可以在一个名叫谷歌趋势的网站上得到这些数据，并且可以下载特定条款或物品被搜索总量的指数。Varian和Choi表示，共同利用这些搜索趋势和经济计量模型就能够提高预测的准确性。

For example, using data on searches for trucks and SUVs to predict the monthly sales of motor vehicles reduces the average error by up to 18% compared with the predictions from a model that did not incorporate the search data. The volume of searches for Hong Kong carried out in countries like America, Britain, Australia and India also seems to predict eventual tourist arrivals to the territory from these countries rather well.

比如，与不用搜索数据的模型得出的预测相比，利用货车和SUV的搜索数据来预测汽车月销售量会减少高达18%的平均误差。在美国，英国，澳大利亚和印度等国进行的对香港的搜索似乎也能预测从这些国家到香港旅行的游客人数。

How widely could this idea be applied? For some things, like retail sales, the categories into which Google classifies its search-trend data correspond closely to what people may want to predict, such as the sales of a particular brand of car (see chart). For others, like sales of houses, things are less clear. It appears that searches for estate agents work better than those for