Work

Forecasting Spanish unemployment with Google Trends and dimension reduction techniques

Year: 2021

Type: article

Abstract: 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.

Source: SERIEs

DOI: 10.1007/s13209-021-00231-x

Authors: Rodrigo Mulero, Alfredo García-Hiernaux

Institution: Universidad Complutense de Madrid

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid:

Funder: Fundación Banco Santander