Economic Predictions With Big Data: The Illusion of Sparsity

Domenico Giannone, Michele Lenza and Giorgio Primiceri

Econometrica, 2021, vol. 89, issue 5, 2409-2437

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 model, but on a wide set of models that often include many predictors.

Date: 2021
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Citations: View citations in EconPapers (60)

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https://doi.org/10.3982/ECTA17842

Related works:
Working Paper: Economic predictions with big data: the illusion of sparsity (2021)
Working Paper: Economic Predictions with Big Data: The Illusion of Sparsity (2018)
Working Paper: Economic predictions with big data: the illusion of sparsity (2018)
Working Paper: Economic Predictions with Big Data: The Illusion Of Sparsity (2017)
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