Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/224160
Authors: 
Beyeler, Simon
Kaufmann, Sylvia
Year of Publication: 
2018
Series/Report no.: 
Working Paper No. 16.08R
Abstract: 
We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings identify the unobserved factors and provide a meaningful economic interpretation for them. Applying our methodology to US macroeconomic data reveals indeed a high degree of sparsity in the data. We use the estimated FAVAR to study the effect of a monetary policy shock and a shock to the term premium. Factors and specific variables show sensible responses to the identified shocks.
Subjects: 
Bayesian FAVAR
sparsity
factor identification
JEL: 
C32
C55
E32
E43
E52
Document Type: 
Working Paper

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