Economic predictions with big data: the illusion of sparsity

Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.

Report

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;