This paper presents a method to improve the one-step-ahead forecasts of the Spanish unemployment monthly series. To do so, we use numerous potential explanatory variables extracted from searches in Google (Google Trends tool). Two different dimension reduction techniques are implemented (PCA and Forward Stepwise Selection) to decide how to combine the explanatory variables or which ones to use. The results of a recursive forecasting exercise reveal a statistically significant increase in predictive accuracy of 10–25%, depending on the dimension reduction method employed. A deep robustness analysis confirms these findings, as well as the relevance of using a large amount of Google queries together with a dimension reduction technique, when no prior information on which are the most informative queries is available.

本文提出了一种改进西班牙失业月度序列的单步预测的方法。为此，我们使用了许多潜在的解释变量，这些变量是从Google搜索（Google趋势工具）中提取的。实现了两种不同的降维技术（PCA和正向逐步选择），以决定如何组合解释变量或使用哪些变量。递归预测练习的结果表明，根据所采用的降维方法，预测准确性的统计上显着提高了10–25％。如果没有关于信息量最大的查询的先前信息可用，那么深入的健壮性分析将证实这些发现，以及使用大量Google查询和降维技术的相关性。