Andrei Zeleneev, Weisheng Zhang
arXiv 19 Nov 2025 · Econometrics
arXiv:2511.15427 · PDF · DOI · OpenAlex · Extracted main text
Interactive fixed effects are routinely controlled for in linear panel models. While an analogous fixed effects (FE) estimator for nonlinear models has been available in the literature (Chen, Fernandez-Val and Weidner, 2021), it sees much more limited use in applied research because its implementation involves solving a high-dimensional non-convex problem. In this paper, we complement the theoretical analysis of Chen, Fernandez-Val and Weidner (2021) by providing a new computationally efficient estimator that is asymptotically equivalent to their estimator. Unlike the previously proposed FE estimator, our estimator avoids solving a high-dimensional optimization problem and can be feasibly computed in large nonlinear panels. Our proposed method involves two steps. In the first step, we convexify the optimization problem using nuclear norm regularization (NNR) and obtain preliminary NNR estimators of the parameters, including the fixed effects. Then, we find the global solution of the original optimization problem using a standard gradient descent method initialized at these preliminary estimates. Thus, in practice, one can simply combine our computationally efficient estimator with the inferential theory provided in Chen, Fernandez-Val and Weidner (2021) to construct confidence intervals and perform hypothesis testing.
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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 | Chen, Mingli and Fernández-Val, Iván and Weidner, Martin (2021) Nonlinear factor models for network and panel data | 0.980 | 51 | 9 | 94% |
| 2 | Ma, Shujie and Su, Liangjun and Zhang, Yichong (2022) Detecting latent communities in network formation models | 0.894 | 7 | 4 | 71% |
| 3 | Moon, Hyungsik Roger and Weidner, Martin (2018) Nuclear norm regularized estimation of panel regression models | 0.874 | 18 | 2 | 100% |
| 4 | Chernozhukov, Victor and Hansen, Christian Bailey and Liao, Yuan and… (2019) Inference for heterogeneous effects using low-rank estimations | 0.874 | 15 | 5 | 67% |
| 5 | Su, Liangjun and Wang, Fa and Wang, Yiren (2025) Estimation and Inference for Unbalanced Panel Data Models with Interactive Fixed Effects | 0.874 | 5 | 2 | 100% |
| 6 | Fernández-Val, Iván and Weidner, Martin (2016) Individual and time effects in nonlinear panel models with large N, T | 0.644 | 4 | 2 | 50% |
| 7 | Armstrong, Timothy B and Weidner, Martin and Zeleneev, Andrei (2022) Robust estimation and inference in panels with interactive fixed effects self | 0.644 | 2 | 2 | 100% |
| 8 | Hastie, Trevor and Tibshirani, Robert and Wainwright, Martin (2015) Statistical learning with sparsity | 0.644 | 2 | 2 | 100% |
| 9 | Chen, Mingli (2016) Estimation of nonlinear panel models with multiple unobserved effects | 0.585 | 3 | 1 | 100% |
| 10 | Bai, Jushan (2009) Panel data models with interactive fixed effects | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects | 1.000 | 16 | 5 |
| 2 | 0.5cmLow-Rank Estimation of Nonlinear Panel Data Models | 0.405 | 1 | 1 |