  This paper focuses on estimating the coefficients and average partial effects
of observed regressors in nonlinear panel data models with interactive fixed
effects, using the common correlated effects (CCE) framework. The proposed
two-step estimation method involves applying principal component analysis to
estimate latent factors based on cross-sectional averages of the regressors in
the first step, and jointly estimating the coefficients of the regressors and
factor loadings in the second step. The asymptotic distributions of the
proposed estimators are derived under general conditions, assuming that the
number of time-series observations is comparable to the number of
cross-sectional observations. To correct for asymptotic biases of the
estimators, we introduce both analytical and split-panel jackknife methods, and
confirm their good performance in finite samples using Monte Carlo simulations.
An empirical application utilizes the proposed method to study the arbitrage
behaviour of nonfinancial firms across different security markets.
