On the (Mis)Use of Machine Learning With Panel Data
Augusto Cerqua, Marco Letta, Gabriele Pinto
Abstract
ABSTRACT We provide the first systematic assessment of data leakage issues in the use of machine learning on panel data. Our organising framework clarifies why neglecting the cross‐sectional and longitudinal structure of these data leads to hard‐to‐detect data leakage, inflated out‐of‐sample performance, and an inadvertent overestimation of the real‐world usefulness and applicability of machine learning models. We then offer empirical guidelines for practitioners to ensure the correct implementation of supervised machine learning in panel data environments. An empirical application, using data from over 3000 U.S. counties spanning 2000 to 2019 and focused on income prediction, illustrates the practical relevance of these points across nearly 500 models for both classification and regression tasks.