Linear estimations of dynamic fixed effects logit models only with time effects

TL;DR

This paper introduces linear estimation methods for dynamic fixed effects logit models with only time effects, achieving point identification and root-N consistency when five or more time periods are available.

Contribution

It develops linear estimators that identify parameters in dynamic fixed effects logit models with only time effects, enabling root-N consistent estimation.

Findings

Linear estimators can identify parameters with five or more time periods.

Root-N consistent estimation is achievable for these models.

Monte Carlo simulations support the theoretical results.

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.