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Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach

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  • Gary Cornwall
  • Marina Gindelsky

Abstract

Inequality statistics are usually calculated from high-quality microdata, typically available with a one-to-two-year lag. In turbulent times, policymakers and data users need timely estimates. We use an elastic net to nowcast the overall Gini coefficient and quintile-level income shares of personal income published by the US Bureau of Economic Analysis. The nowcasts (2020–2023) predict turning points with at least 90 percent accuracy across all metrics and significantly less error than naive models. We find that we can create advance inequality estimates approximately one month after the end of the calendar year, reducing the present lag by almost a year.

Suggested Citation

  • Gary Cornwall & Marina Gindelsky, 2025. "Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach," AEA Papers and Proceedings, American Economic Association, vol. 115, pages 79-84, May.
    • Handle: RePEc:aea:apandp:v:115:y:2025:p:79-84
    DOI: 10.1257/pandp.20251105
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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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