Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/319320 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 44 [Issue:] 2 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2024 [Pages:] 255-269
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
We investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed‐frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed‐frequency data. We propose a novel cross‐validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross‐validation method outperforms the other specifications in most cases.
Subjects: 
elastic net
high‐dimensional
soft‐thresholding
targeted predictors
variable selection
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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