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Economic predictions with big data: the illusion of sparsity


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;

JEL Classification: C11; C53; C55;

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Bibliographic Information

Provider: Federal Reserve Bank of New York

Part of Series: Staff Reports

Publication Date: 2018-04-01

Number: 847