Economic predictions with big data: the illusion of sparsity

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 847, Staff Reports from Federal Reserve Bank of New York

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.

Keywords: model selection; shrinkage; high dimensional data (search for similar items in EconPapers)
JEL-codes: C11 C53 C55 (search for similar items in EconPapers)
Date: 2018-04-01
New Economics Papers: this item is included in nep-big, nep-ecm, nep-ore and nep-pay
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Citations: View citations in EconPapers (22)

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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 (2017)
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