Forecasting Spanish unemployment with Google Trends and dimension reduction techniques

Artikel

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 (GoogleTrends 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: SERIEs - Journal of the Spanish Economic Association ; ISSN: 1869-4195 ; Volume: 12 ; Year: 2021 ; Issue: 3 ; Pages: 329-349

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods
• Subject: Unemployment ; Forecasting ; Dimensionalityreduction ; RMSE

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mulero, Rodrigo ; Garcia-Hiernaux, Alfredo
• Event: Veröffentlichung
• (who): Springer
• (where): Heidelberg
• (when): 2021

Further information

• DOI: doi:10.1007/s13209-021-00231-x
• Last update: 10.03.2025, 11:44 AM CET