A Bayesian Framework for Sparse Estimation in High-Dimensional Mixed Frequency Vector Autoregressive Models

DOI10.5705/ss.202021.0206OpenAlexW4221115534WikidataQ114013787 ScholiaQ114013787MaRDI QIDQ6069889

Kshitij Khare, George Michailidis, Unnamed Author

Publication date: 17 November 2023

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.202021.0206

zbMATH Keywords

high-dimensional datapseudo-likelihoodnowcastingspike and slab priormixed frequenciesstrong selection consistency

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Regularized estimation in sparse high-dimensional time series models
• MIDAS Regressions: Further Results and New Directions
• Sparse inverse covariance estimation with the graphical lasso
• Macroeconomics and the reality of mixed frequency data
• Computationally efficient inference in large Bayesian mixed frequency VARs
• Strong selection consistency of Bayesian vector autoregressive models based on a pseudo-likelihood approach
• High-dimensional macroeconomic forecasting and variable selection via penalized regression
• Structural Vector Autoregressive Analysis
• Economic Predictions With Big Data: The Illusion of Sparsity

This page was built for publication: A Bayesian Framework for Sparse Estimation in High-Dimensional Mixed Frequency Vector Autoregressive Models