© The Author 2015. Maximum-likelihood estimation of nonlinear models with fixed effects is subject to the incidentalparameter problem. This typically implies that point estimates suffer from large bias and confidence intervals have poor coverage. This article presents a jackknife method to reduce this bias and to obtain confidence intervals that are correctly centred under rectangular-array asymptotics.The method is explicitly designed to handle dynamics in the data, and yields estimators that are straightforward to implement and can be readily applied to a range of models and estimands. We provide distribution theory for estimators of model parameters and average effects, present validity tests for the jackknife, and consider extensions to higher-order bias correction and to two-step estimation problems. An empirical illustration relating to female labour-force participation is also provided.