Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/156502
Authors: 
Beyeler, Simon
Kaufmann, Sylvia
Year of Publication: 
2016
Series/Report no.: 
Working Paper, Study Center Gerzensee 16.08
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. Given that we work with a general covariance matrix of factor innovations, we can implement different strategies for structural shock identification. Applying our methodology to US macroeconomic data (FRED QD) reveals indeed a high degree of sparsity in the data. The proposed identification procedure yields seven unobserved factors that account for about 52 percent of the variation in the data. We simultaneously identify a monetary policy, a productivity and a news shock by recursive ordering and by applying the method of maximizing the forecast error variance share in a specific variable. Factors and specific variables show sensible responses to the identified shocks.
Subjects: 
Bayesian FAVAR
sparsity
factor identification
JEL: 
C32
E32
Document Type: 
Working Paper

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