Economic Predictions with Big Data: The Illusion Of Sparsity

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 12256, CEPR Discussion Papers from Centre for Economic Policy Research

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 “spike-and-slab†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.

Date: 2017-08
New Economics Papers: this item is included in nep-big and nep-pay
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Citations: View citations in EconPapers (28)

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Related works:
Journal Article: Economic Predictions With Big Data: The Illusion of Sparsity (2021)
Working Paper: Economic predictions with big data: the illusion of sparsity (2021)
Working Paper: Economic Predictions with Big Data: The Illusion of Sparsity (2018)
Working Paper: Economic predictions with big data: the illusion of sparsity (2018)
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