Bayesian Dynamic Factor Models is a latent variable model in Bayesian time series analysis that extends static factor models by allowing time-varying factors and loadings, incorporating prior distributions to estimate the evolution of unobserved states through recursive Bayesian updating.
84
Publications
5.2K
Citations
163
Authors
117
Institutions
Publications per year
2017–2026
22
Publications
84
1–5 of 84
Authors
163
- CH
5
h-index
- CO
5
h-index
- MA
4
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- MW
4
h-index
- KB
3
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1–5 of 163
Institutions
117
Tinbergen Institute
Rotterdam, The Netherlands
7 h-index
7 publications
230 citations
International Monetary Fund
Washington D.C., United States
7 h-index
7 publications
1.4K citations
BI Norwegian Business School
Oslo, Norway
5 h-index
7 publications
438 citations
University of Virginia
Charlottesville, United States
4 h-index
6 publications
1.5K citations
Federal Reserve Board of Governors
Washington D.C., United States
4 h-index
4 publications
117 citations
Duke University
Durham, United States
4 h-index
8 publications
766 citations
University of Johannesburg
Johannesburg, South Africa
3 h-index
4 publications
73 citations
The University of Texas at Austin
Austin, United States
3 h-index
6 publications
220 citations
1–8 of 117
Venues
- SSRN Electronic Journal
Repository · 14 publications · 563 citations
12
h-index
- Econstor (Econstor)
Repository · 4 publications · 92 citations
4
h-index
- Journal of Business and Economic Statistics
JBES · 4 publications · 783 citations
4
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- International Journal of Forecasting
3 publications · 97 citations
3
h-index
- RePEc: Research Papers in Economics
Repository · 3 publications · 78 citations
3
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1–5