Abstract: Approximating time-varying unobserved heterogeneity by discrete types has become increasingly popular in economics. Yet, provably valid post-clustering inference for target parameters in models that do not impose an exact group structure is still lacking. This paper fills this gap in the leading case of a linear panel data model with nonseparable two-way unobserved heterogeneity. Building on insights from the double machine learning literature, we propose a simple inference procedure based on a bias-reducing moment. Asymptotic theory and simulations suggest excellent performance. In the application on fiscal policy we revisit, the novel approach yields conclusions in line with economic theory.
Inference after discretizing time-varying unobserved heterogeneity
Speaker
Jad Beyhum (KU Leuven)
Date & Time
From: 14 October 2025, 11:30
Until: 14 October 2025, 12:30
Type
Seminar
Venue
UCL, 25 Gordon Street, Maths 500, WC1H 0AY