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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.03663 |
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| _version_ | 1866913005015400448 |
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| author | Yan, Zizhong Zhang, Zhengyu Chen, Mingli Li, Jingrong Fernández-Val, Iván |
| author_facet | Yan, Zizhong Zhang, Zhengyu Chen, Mingli Li, Jingrong Fernández-Val, Iván |
| contents | We develop likelihood-based bias reduction for nonlinear panel models with additive individual and time effects. In two-way panels, integrated-likelihood corrections are attractive but challenging because the required integration is high dimensional and standard Laplace approximations may fail when the parameter dimension grows with the sample size. We propose a target-centered full-exponential Laplace--cumulant expansion that exploits the sparse higher-order derivative structure implied by additive effects, delivering a tractable approximation with a negligible remainder under large-$N,T$ asymptotics. The expansion motivates robust priors that yield bias reduction for both common parameters and fixed effects. We provide implementations for binary, ordered, and multinomial response models with two-way effects. For average partial effects, we show that the remaining first-order bias has a simple variance form and can be removed by a closed-form adjustment. Monte Carlo experiments and an empirical illustration show substantial bias reduction with accurate inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03663 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Priors in Nonlinear Panel Models with Individual and Time Effects Yan, Zizhong Zhang, Zhengyu Chen, Mingli Li, Jingrong Fernández-Val, Iván Econometrics Statistics Theory We develop likelihood-based bias reduction for nonlinear panel models with additive individual and time effects. In two-way panels, integrated-likelihood corrections are attractive but challenging because the required integration is high dimensional and standard Laplace approximations may fail when the parameter dimension grows with the sample size. We propose a target-centered full-exponential Laplace--cumulant expansion that exploits the sparse higher-order derivative structure implied by additive effects, delivering a tractable approximation with a negligible remainder under large-$N,T$ asymptotics. The expansion motivates robust priors that yield bias reduction for both common parameters and fixed effects. We provide implementations for binary, ordered, and multinomial response models with two-way effects. For average partial effects, we show that the remaining first-order bias has a simple variance form and can be removed by a closed-form adjustment. Monte Carlo experiments and an empirical illustration show substantial bias reduction with accurate inference. |
| title | Robust Priors in Nonlinear Panel Models with Individual and Time Effects |
| topic | Econometrics Statistics Theory |
| url | https://arxiv.org/abs/2604.03663 |