Economic Predictions with Big Data: The Illusion of Sparsity

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 20180521, Liberty Street Economics from Federal Reserve Bank of New York

Abstract: The availability of large data sets, combined with advances in the fields of statistics, machine learning, and econometrics, have generated interest in forecasting models that include many possible predictive variables. Are economic data sufficiently informative to warrant selecting a handful of the most useful predictors from this larger pool of variables? This post documents that they usually are not, based on applications in macroeconomics, microeconomics, and finance.

Keywords: Shrinkage; High Dimensional Data; Model Selection (search for similar items in EconPapers)
JEL-codes: E17 (search for similar items in EconPapers)
Date: 2018-05-21
New Economics Papers: this item is included in nep-big
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Related works:
Journal Article: Economic Predictions With Big Data: The Illusion of Sparsity (2021)
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 (2017)
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