Linear estimations of dynamic fixed effects logit models only with time effects
TLDR
This paper introduces linear estimation methods for dynamic fixed effects logit models with time effects, achieving root-N consistent estimations.
Key contributions
- Proposes novel linear estimation methods for dynamic fixed effects logit models with time effects.
- Methods point-identify parameters of interest given five or more time periods.
- Achieves root-N consistent estimations for these specific logit models.
- Monte Carlo simulations validate the effectiveness of the proposed estimators.
Why it matters
This paper offers a significant advancement in econometric modeling by providing root-N consistent linear estimators for dynamic fixed effects logit models. This improves the accuracy and reliability of estimations in scenarios with only time effects, benefiting researchers and practitioners.
Original Abstract
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.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.