Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/210699
Authors: 
Giannone, Domenico
Lenza, Michele
Primiceri, Giorgio E.
Year of Publication: 
2018
Series/Report no.: 
Staff Report No. 847
Abstract: 
We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can only emerge when models of very low dimension are strongly favored a priori.
Subjects: 
model selection
shrinkage
high dimensional data
JEL: 
C11
C53
C55
Document Type: 
Working Paper

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