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...

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, [[EQUATION]], and weather data for Oslo, Norway....

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...

We provide a quantification of the impact of the recent US-China political tensions on talent flows from China to the US through the lens of monthly air passenger traffic data. By exploring variations across origin countries and geographical locations of US airports as well as differences over...

China's approach to developing a world-class science system includes a vigorous set of programmes to attract back Chinese researchers who have overseas training and work experience. No analysis is available to show the performance of these mobile researchers nor do we know the exact impact of...