Economic Predictions with Big Data: The Illusion of Sparsity
29 Pages Posted: 7 May 2018
Date Written: April , 2018
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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