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Economic Predictions with Big Data: The Illusion of Sparsity

Author

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  • Domenico Giannone
  • Michele Lenza
  • Giorgio E. Primiceri

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.

Suggested Citation

  • Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic Predictions with Big Data: The Illusion of Sparsity," Liberty Street Economics 20180521, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednls:87258
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    More about this item

    Keywords

    Shrinkage; High Dimensional Data; Model Selection;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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    This paper has been announced in the following NEP Reports:

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