arXiv 27 Apr 2026 · Econometrics
arXiv:2604.24150 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes linear estimation methods for dynamic fixed effects logit models only with time effects (i.e., those only with time dummies and only with time trends). The linear estimators point-identify transformations of parameters of interest for the models if five or more time periods are provided and then point-identify the parameters of interest. What it boils down to is that root-N consistent estimations are attainable for these models. Monte Carlo results corroborate this conclusion.
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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 | Honoré and Weidner (2025) Moment conditions for dynamic panel logit models with fixed effects | 0.928 | 4 | 3 | 100% |
| 2 | Honoré and Tamer (2006) Bounds on parameters in panel dynamic discrete choice models | 0.874 | 7 | 2 | 100% |
| 3 | Maxima (2025) Maxima, a Computer Algebra System. Version 5.48.1 | 0.843 | 4 | 3 | 75% |
| 4 | Dobronyi, Gu and Kim (2021) Identification of dynamic panel logit models with fixed effects | 0.811 | 4 | 2 | 100% |
| 5 | Dano (2023) Transition probabilities and identifying moments in dynamic fixed effects logit models | 0.737 | 3 | 2 | 100% |
| 6 | Kitazawa (2022) Transformations and moment conditions for dynamic fixed effects logit models self | 0.644 | 2 | 2 | 100% |
| 7 | Honoré and Kyriazidou (2000) Panel data discrete choice models with lagged dependent variables | 0.585 | 3 | 1 | 100% |
| 8 | Hahn (2001) The information bound of a dynamic panel logit model with fixed effects | 0.511 | 2 | 1 | 100% |
| 9 | Bezanson, Edelman, Karpinski and Shah (2017) Julia: A fresh approach to numerical computing | 0.405 | 1 | 1 | 100% |
| 10 | Bonhomme (2012) Functional differencing | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.