Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286537 
Year of Publication: 
2021
Citation: 
[Journal:] SERIEs - Journal of the Spanish Economic Association [ISSN:] 1869-4195 [Volume:] 12 [Issue:] 3 [Year:] 2021 [Pages:] 329-349
Publisher: 
Springer, Heidelberg
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 (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.
Subjects: 
Unemployment
Forecasting
Dimensionalityreduction
RMSE
JEL: 
C32
C52
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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