RT Journal Article
T1 Forecasting Spanish unemployment with Google Trends and dimension reduction techniques
A1 Mulero, Rodrigo
A1 García Hiernaux, Alfredo
AB 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.
YR 2020
FD 2020
LK https://hdl.handle.net/20.500.14352/6356
UL https://hdl.handle.net/20.500.14352/6356
LA eng
NO Universidad Complutense de Madrid/Banco de Santander
DS Docta Complutense
RD 14 may 2024