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

Inequality statistics are usually calculated from high-quality, comprehensive survey or administrative microdata. Naturally, this data is typically available with a lag of at least 9 months from the reference period. In turbulent times, there is interest in knowing the distributional impacts of observable aggregate business cycle and policy changes sooner. In this paper, we use an elastic net, a generalized model that incorporates lasso and ridge regressions as special cases, to nowcast the overall Gini coefficient and quintile-level income shares. We use national accounts data starting in 2000, published by the Bureau of Economic Analysis, as features instead of the underlying microdata to produce a series of distributional nowcasts for 2020-2022. 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.