High-Dimensional Panel Data Models with Interactive Fixed Effects: Beyond the Linear Case

Maximilian Rücker, Michael Vogt and Oliver B. Linton

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: Modern economic panel data sets are often high-dimensional: they contain information on a wide variety of control variables whose number may even exceed the sample size. Nevertheless, the literature on econometric methods for high-dimensional panels is quite limited. In this paper, we study high-dimensional panel models with interactive fixed effects where the regression function has an additive structure, i.e., each covariate enters the model via an unknown nonlinear component function. We develop estimation methodology and theory in this additive framework which substantially extends previous work on the high-dimensional linear case by Rücker et al. (2025). In the theoretical part of the paper, we derive the convergence rate of our estimator for both the small-T and the large-T panel case. The theory is complemented by comprehensive Monte Carlo experiments and an empirical application.

Keywords: High-Dimensional Panel Data; Interactive Fixed Effects; Additive Models Lasso (search for similar items in EconPapers)
JEL-codes: C13 C23 C55 (search for similar items in EconPapers)
Date: 2026-08-03
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