Xuan Leng, Jiaming Mao, Yutao Sun
arXiv 4 May 2023 · Econometrics · publishedEconometrics Journal (2026)
arXiv:2305.03134 · PDF · DOI · OpenAlex · Extracted main text
We introduce a generic class of dynamic nonlinear heterogeneous parameter models that incorporate individual and time fixed effects in both the intercept and slope. These models are subject to the incidental parameter problem, in that the limiting distribution of the point estimator is not centered at zero, and that test statistics do not follow their standard asymptotic distributions as in the absence of the fixed effects. To address the problem, we develop an analytical bias correction procedure to construct a bias-corrected likelihood. The resulting estimator follows an asymptotic normal distribution with mean zero. Moreover, likelihood-based tests statistics -- including likelihood-ratio, Lagrange-multiplier, and Wald tests -- follow the limiting chi-squared distribution under the null hypothesis. Simulations demonstrate the effectiveness of the proposed correction method, and an empirical application on the labor force participation of single mothers underscores its practical importance.
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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 | Bester, C. A. and C. Hansen (2009) A penalty function approach to bias reduction in nonlinear panel models with fixed effects | 0.754 | 7 | 3 | 43% |
| 2 | Dhaene, G. and K. Jochmans (2015) Split-panel jackknife estimation of fixed-effect models | 0.737 | 4 | 3 | 50% |
| 3 | Hahn, J. and W. Newey (2004) Jackknife and analytical bias reduction for nonlinear panel models | 0.737 | 4 | 3 | 50% |
| 4 | Lu, X. and L. Su (2023) Uniform inference in linear panel data models with two-dimensional heterogeneity | 0.693 | 6 | 3 | 33% |
| 5 | Arellano, M. and J. Hahn (2016) A likelihood-based approximate solution to the incidental parameter problem in dynamic nonlinear models with multiple effects | 0.644 | 15 | 4 | 27% |
| 6 | Angrist, J. D. and W. N. Evans (1998) Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size | 0.644 | 2 | 2 | 100% |
| 7 | Higgins, A. and K. Jochmans (2024) Bootstrap inference for fixed-effect models | 0.644 | 2 | 2 | 100% |
| 8 | Fernández-Val, I. and M. Weidner (2016) Individual and time effects in nonlinear panel models with large N,T | 0.523 | 21 | 4 | 14% |
| 9 | Hahn, J. and G. Kuersteiner (2011) Bias reduction for dynamic nonlinear panel models with fixed effects | 0.511 | 4 | 2 | 25% |
| 10 | Chamberlain, G (1980) Analysis of covariance with qualitative data | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 74 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Robust Priors in Nonlinear Panel Models with Individual and Time Effects | 0.644 | 2 | 2 |
| 2 | Penalized Likelihood for Dyadic Network Formation Models with Degree Heterogeneity | 0.511 | 2 | 1 |