### Crossover Jackknife Bias Correction for Nonstationary Panel

with Victor Chernozhukov, Ivan Fernandez-Val, Hiroyuki Kasahara and Paul Schrimpf

**Abstract:** Fixed effects estimators suffer from the incidental parameter problem in dynamic or nonlinear panel models with unobserved effects. Hahn and Newey (2004) and Dhaene and Jochmans (2015) proposed convenient jackknife bias corrections, which require that all the variables in the panel be stationary over time. Many covariates of interest in panel and difference-in-differences applications such as policy indicators, age or cohort are not stationary over time. We propose a jackknife bias correction for fixed effects estimators that does not rely on stationarity. We name the new correction as crossover jackknife as it is based on partitioning the panel in two halves, each including half of the time series observations for each cross sectional unit. Numerical examples show that crossover jackknife improves over the existing jackknife corrections, which are not even applicable under some common forms of non-stationarity such as a policy intervention that starts in the middle of the time dimension for some of the cross sectional units.