英文文献：In Search of a Job: Forecasting Employment Growth in the US using Google Trends-在寻找工作:预测就业增长在美国使用谷歌趋势
英文文献作者：Erik Christian Montes Schütte
英文文献摘要：
We show that Google search activity on relevant terms is a strong out-of-sample predictor of future employment growth in the US and that it greatly outperforms benchmark predictive models based on macroeconomic, financial, and sentiment variables. Using a subset of ten keywords, we construct a panel with 211 variables using Google’s own algorithms to find related search queries. We use Elastic Net variable selection in combination with Partial Least Squares to extract the most important information from a large set of search terms. Our forecasting model, which can be constructed in real time and is free from revisions, delivers an out-of-sample R^2 statistic of 65% to 88% for horizons between one month and one year ahead over the period 2008-2017, which compares to between roughly 30% and 60% for the benchmark models.

我们表明，谷歌相关词条的搜索活动是美国未来就业增长的一个强有力的样本外预测指标，而且它的表现大大超过基于宏观经济、金融和情绪变量的基准预测模型。使用10个关键字的子集，我们使用谷歌自己的算法构建一个有211个变量的面板来查找相关的搜索查询。利用弹性网络变量选择与偏最小二乘相结合的方法，从大量的搜索项中提取最重要的信息。我们的预测模型可以实时构建，并且不需要修改，在2008-2017年期间，一个月到一年的时间段内，样本外R^2统计量为65%到88%，相比之下，基准模型大约为30%到60%。