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

Author

Listed:
  • Rücker, M.
  • Vogt, M.
  • Linton, O. B.

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.

Suggested Citation

  • Rücker, M. & Vogt, M. & Linton, O. B., 2026. "High-Dimensional Panel Data Models with Interactive Fixed Effects: Beyond the Linear Case," Cambridge Working Papers in Economics 2665, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2665
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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