Econometrica: Sep 2021, Volume 89, Issue 5

Economic Predictions with Big Data: The Illusion of Sparsity

https://doi.org/10.3982/ECTA17842
p. 2409-2437

Domenico Giannone, Michele Lenza, Giorgio E. Primiceri

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.



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Supplemental Material

Supplement to "Economic Predictions with Big Data: The Illusion of Sparsity"

This document contains some additional results and technical details not included in the main body of the paper. In particular, we present: i) more Monte Carlo simulation evidence; ii) the details of our out-of-sample forecasting exercise. This supplement is not self-contained, so readers are advised to read the main paper first.

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Supplement to "Economic Predictions with Big Data: The Illusion of Sparsity"

This zip file contains only the replication codes (without data and posterior simulation results).

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Supplement to "Economic Predictions with Big Data: The Illusion of Sparsity"

This zip file contains only one file, i.e. the main estimation function (the top of the file contains a thorough description).

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Supplement to "Economic Predictions with Big Data: The Illusion of Sparsity"

This zip file contains the full replication material for the manuscript.

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