Root-n-consistent Conditional ML estimation of dynamic panel logit models with fixed effects
Econometrics
2025-09-03 v6
Abstract
In this paper we first propose a root-n-consistent Conditional Maximum Likelihood (CML) estimator for all the common parameters in the panel logit AR(p) model with strictly exogenous covariates and fixed effects. Our CML estimator (CMLE) converges in probability faster and is more easily computed than the kernel-weighted CMLE of Honor\'e and Kyriazidou (2000). Next, we propose a root-n-consistent CMLE for the coefficients of the exogenous covariates only. We also discuss new CMLEs for the panel logit AR(p) model without covariates. Finally, we propose CMLEs for multinomial dynamic panel logit models with and without covariates. All CMLEs are asymptotically normally distributed.
Keywords
Cite
@article{arxiv.2103.04973,
title = {Root-n-consistent Conditional ML estimation of dynamic panel logit models with fixed effects},
author = {Hugo Kruiniger},
journal= {arXiv preprint arXiv:2103.04973},
year = {2025}
}
Comments
This paper contains a serious mistake that cannot be corrected