Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230128
Authors: 
Maas, Benedikt
Year of Publication: 
2019
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 39 [Issue:] 3 [Pages:] 394-411
Abstract: 
This paper aims to assess whether Google search data are useful when predicting the US unemployment rate among other more traditional predictor variables. A weekly Google index is derived from the keyword “unemployment” and is used in diffusion index variants along with the weekly number of initial claims and monthly estimated latent factors. The unemployment rate forecasts are generated using MIDAS regression models that take into account the actual frequencies of the predictor variables. The forecasts are made in real time, and the forecasts of the best forecasting models exceed, for the most part, the root mean squared forecast error of two benchmarks. However, as the forecasting horizon increases, the forecasting performance of the best diffusion index variants decreases over time, which suggests that the forecasting methods proposed in this paper are most useful in the short term.
Subjects: 
MIDAS
Google data
forecast comparison
US unemployment
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by-nc-nd/4.0/
Document Type: 
Article
Document Version: 
Published Version

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