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A0176
Title: Economic predictions with big data: The illusion of sparsity done Authors:  Giorgio Primiceri - Northwestern University (United States) [presenting]
Abstract: The aim is to 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.