Forecasting Spanish unemployment with Google Trends and dimension reduction techniques

  1. Rodrigo Mulero
  2. Alfredo García-Hiernaux
Revista:
SERIEs : Journal of the Spanish Economic Association

ISSN: 1869-4195

Any de publicació: 2021

Volum: 12

Número: 3

Pàgines: 329-349

Tipus: Article

DOI: 10.1007/S13209-021-00231-X DIALNET GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: SERIEs : Journal of the Spanish Economic Association

Objectius de Desenvolupament Sostenible

Resum

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.

€ Mostra el finançament

Informació de finançament

Finançadors

Fuente de los datos: Dialnet