Forecasting Spanish unemployment with Google Trends and dimension reduction techniques

Rodrigo Mulero () and Alfredo Garcia-Hiernaux
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Rodrigo Mulero: Universidad Complutense de Madrid

SERIEs: Journal of the Spanish Economic Association, 2021, vol. 12, issue 3, No 1, 329-349

Abstract: 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.

Keywords: Unemployment; Forecasting; Google Trends; Dimensionality reduction; RMSE (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:series:v:12:y:2021:i:3:d:10.1007_s13209-021-00231-x

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DOI: 10.1007/s13209-021-00231-x

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