Binzhi Chen, Annalivia Polselli, Paul S. Clarke
arXiv 2 Aug 2026 · Econometrics
arXiv:2608.01137 · PDF · Extracted main text
Factor structures are central to empirical work in economics and finance, and are usually used to model time-varying unobserved heterogeneity through interactive fixed effects (IFE). Existing IFE estimators rest on low-dimensional and linear specifications in the covariates, assumptions which are increasingly restrictive in applications drawing on rich datasets with controls of unknown functional form. This paper develops a Double Machine Learning estimator for the high-dimensional partially linear panel model with interactive fixed effects (panel DML-IFE). The method combines projection-based defactorisation of the data, in the spirit of Common Correlated Effects (CCE), with a Neyman-orthogonal score function and cross-fitting procedure, and accommodates low-rank factor structures in outcomes and treatments alongside high-dimensional, potentially nonlinear covariate effects estimated by machine learning algorithms. Monte Carlo simulations show that panel DML-IFE outperforms conventional IFE estimator outside the correctly-specified linear case, with bias reduction driven primarily by the time and covariate dimensions. An empirical application to U.S. stock returns shows that several effects documented under linear specifications lose statistical significance once high-dimensional nonlinear confounding and the presence of IFE are jointly accounted for.
appendix boundary found by appendix_command · 80% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rücker, Maximilian and Vogt, Michael and Linton, Oliver and Walsh, C… (2025) Estimation and inference in high-dimensional panel data models with interactive fixed effects | 1.000 | 11 | 3 | 100% |
| 2 | Clarke, Paul S and Polselli, Annalivia (2025) Double machine learning for static panel models with fixed effects self | 1.000 | 10 | 5 | 100% |
| 3 | Pesaran, M Hashem (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure | 1.000 | 7 | 3 | 100% |
| 4 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.855 | 8 | 3 | 62% |
| 5 | Bai, Jushan (2009) Panel data models with interactive fixed effects | 0.847 | 23 | 5 | 61% |
| 6 | Moon, Hyungsik Roger and Weidner, Martin (2017) Dynamic linear panel regression models with interactive fixed effects | 0.737 | 3 | 2 | 100% |
| 7 | Fama, Eugene F and French, Kenneth R (1993) Common risk factors in the returns on stocks and bonds | 0.644 | 2 | 2 | 100% |
| 8 | Binzhi Chen (2026) xtife: Interactive Fixed Effects Estimator for Balanced Panel Data self | 0.644 | 2 | 2 | 100% |
| 9 | Moon, Hyungsik Roger and Weidner, Martin (2018) Nuclear norm regularized estimation of panel regression models | 0.585 | 3 | 1 | 100% |
| 10 | Bergstra, James and Bengio, Yoshua (2012) Random search for hyper-parameter optimization. | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 53 scored citations.