Institutional Affiliation: Bureau of Economic Analysis
NBER Working Papers and Publications
|July 2019||Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators|
with Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen
in Big Data for 21st Century Economic Statistics, Katharine G. Abraham, Ron S. Jarmin, Brian Moyer, and Matthew D. Shapiro
Timely alternative data sources such as credit card transactions and search query trends have become more readily available in recent years, while sophisticated machine learning (ML) techniques have enabled marked gains in predictive accuracy. These advances offer the benefit of revealing economic news earlier in the estimation cycle, reducing revisions, and improving estimate quality. But which combinations of data and ML techniques give the most accurate prediction of national economic activity? To answer this question, we conduct a prediction horse race using a one-step ahead model validation design to evaluate how each ML algorithm, data set, and variable selection method weighs on predictive accuracy. We test 73,884 model specifications, consider 1,180 variables drawn from both tradit...