Maximum-likelihood estimation of nonlinear models with fixed effects is subject to the incidental-parameter 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.

Evaluating competing multifactor asset pricing models involves comparing the statistical significance of their mean pricing errors (alphas). Unfortunately, this comparison favors imprecisely estimated models because p-values tend to be higher in more noisy models. To avoid false impressions of relative success at tests for zero mean pricing errors, we develop a notion of comparative p-values and suggest comparing these instead of the raw p-values. This comparison gives more precisely estimated models a fairer chance or, equivalently, quantifies how much easier it is for imprecisely estimated models, by comparison, to pass the test.