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• W3131712552 abstract "Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observations per low-frequency period grow, MF-VARs suffer from the of dimensionality. We curb this curse through a regularizer that permits various hierarchical sparsity patterns by prioritizing the inclusion of coefficients according to the recency of the information they contain. Additionally, we investigate the presence of nowcasting relations by sparsely estimating the MF-VAR error covariance matrix. We study predictive Granger causality relations in a MF-VAR for the U.S. economy and construct a coincident indicator of GDP growth." @default.
• W3131712552 created "2021-03-01" @default.
• W3131712552 creator A5024416689 @default.
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• W3131712552 date "2021-02-23" @default.
• W3131712552 modified "2023-09-27" @default.
• W3131712552 title "Hierarchical regularizers for mixed-frequency vector autoregressions" @default.
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• W3131712552 hasPublicationYear "2021" @default.
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