Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909000915746816 |
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| author | Xu, Haoyuan Miao, Wei Dhaene, Geert Beyhum, Jad |
| author_facet | Xu, Haoyuan Miao, Wei Dhaene, Geert Beyhum, Jad |
| contents | The maximum likelihood estimator in nonlinear panel data models with interactive fixed effects is biased. Several bias correction methods, such as analytical and jackknife approaches, have been proposed to enable valid inference. This paper shows that the parametric bootstrap also enables valid inference in such models. In particular, we show that the parametric bootstrap replicates the asymptotic distribution of the maximum likelihood estimator. Therefore, it yields asymptotically unbiased estimates and confidence sets with asymptotically correct coverage. We also propose a transformation-based bootstrap confidence interval that delivers improved finite-sample performance. Simulation results support the theoretical findings. Finally, we apply the proposed method to examine technological and product market spillover effects on firms' innovation behavior. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_26826 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects Xu, Haoyuan Miao, Wei Dhaene, Geert Beyhum, Jad Econometrics Methodology The maximum likelihood estimator in nonlinear panel data models with interactive fixed effects is biased. Several bias correction methods, such as analytical and jackknife approaches, have been proposed to enable valid inference. This paper shows that the parametric bootstrap also enables valid inference in such models. In particular, we show that the parametric bootstrap replicates the asymptotic distribution of the maximum likelihood estimator. Therefore, it yields asymptotically unbiased estimates and confidence sets with asymptotically correct coverage. We also propose a transformation-based bootstrap confidence interval that delivers improved finite-sample performance. Simulation results support the theoretical findings. Finally, we apply the proposed method to examine technological and product market spillover effects on firms' innovation behavior. |
| title | Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2604.26826 |