Forecasting Spanish unemployment with Google Trends and dimension reduction techniques

Abstract

This paper presents a method to improve the one-step-ahead forecasts of the Spanish unemployment monthly series. To do so, we use a large number of potential explanatory variables extracted from searches in Google (Google Trends tool). Two different dimension reduction techniques are implemented to decide how to combine the explanatory variables or which ones to use. The results reveal an increase in predictive accuracy of 10-25%, depending on the dimension reduction method employed. A deep robustness analysis confirms this 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.