Machine learning panel data regressions with heavy-tailed dependent data: Theory and application
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DOI: 10.1016/j.jeconom.2022.07.001
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- Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
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Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
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"Dynamic portfolio selection with sector-specific regularization,"
LIDAM Discussion Papers ISBA
2020032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints LFIN 2022007, Université catholique de Louvain, Louvain Finance (LFIN).
- Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints ISBA 2022013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
- Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
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Keywords
High-dimensional panels; Large N and T panels; Mixed-frequency data; Sparse-group LASSO; Fat tails;All these keywords.
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