  Fixed effect estimators of nonlinear panel data models suffer from the
incidental parameter problem. This leads to two undesirable consequences in
applied research: (1) point estimates are subject to large biases, and (2)
confidence intervals have incorrect coverages. This paper proposes a
simulation-based method for bias reduction. The method simulates data using the
model with estimated individual effects, and finds values of parameters by
equating fixed effect estimates obtained from observed and simulated data. The
asymptotic framework provides consistency, bias correction, and asymptotic
normality results. An application and simulations to female labor force
participation illustrates the finite-sample performance of the method.
