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Hierarchical regularizers for mixed-frequency vector autoregressions
Alain Hecq
,
Marie Ternes
,
Ines Wilms
QE Econometrics
GSBE other - not theme-related research
Mathematics Centre Maastricht
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Working paper
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Dive into the research topics of 'Hierarchical regularizers for mixed-frequency vector autoregressions'. Together they form a unique fingerprint.
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Keyphrases
Mixed-frequency Vector Autoregression
100%
Regularizer
100%
Granger Causality
25%
Vector Autoregression Model
25%
Curb
25%
Recency
25%
Sparsity Pattern
25%
Nowcasting
25%
GDP Growth
25%
Coincident Indicators
25%
High-frequency Observations
25%
US Economy
25%
Causality Relationship
25%
Hierarchical Sparsity
25%
MF-VAR
25%
Error Covariance Matrix
25%
Curse of Dimensionality
25%
INIS
vectors
100%
matrices
25%
information
25%
errors
25%
inclusions
25%
dynamics
25%
economy
25%
indicators
25%
growth
25%
causality
25%
Engineering
Autoregression
100%
Dimensionality
33%
Sparsity Pattern
33%
Autoregression Model
33%
Error Covariance Matrix
33%
Curbs
33%
Economics, Econometrics and Finance
Autoregression
100%
Causality Analysis
25%
Psychology
Nowcasting
100%