We propose a bias correction method for nonlinear models with both individual and time effects. Under the presence of the incidental parameter problem, the maximum likelihood estimator derived from such models may be severely biased. Our method produces an approximation to an infeasible...

We introduce a second-order correction technique for nonlinear fixed-effect models exposed to the incidental parameter problem. This technique produces a bias-corrected log-likelihood function that possesses a bias only to the order (in expectation) of O (T-3) where T is the number of time...

We introduce a new Stata program xtspj as a bias correction tool for nonlinear models with fixed effects. The correction removes the first-order bias term using the split-panel jackknife estimation technique introduced by Dhaene and Jochmans (2015). Both the jackknife of the parameter and the...