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.
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Provider: Federal Reserve Bank of New York
Part of Series: Staff Reports
Publication Date: 2018-04-01