This paper presents a new approach to estimation and inference in panel data
models with interactive fixed effects, where the unobserved factor loadings are
allowed to be correlated with the regressors. A distinctive feature of the
proposed approach is to assume a nonparametric specification for the factor
loadings, that allows us to partial out the interactive effects using sieve
basis functions to estimate the slope parameters directly. The new estimator
adopts the well-known partial least squares form, and its
NT​-consistency and asymptotic normality are shown. Later, the common
factors are estimated using principal component analysis (PCA), and the
corresponding convergence rates are obtained. A Monte Carlo study indicates
good performance in terms of mean squared error. We apply our methodology to
analyze the determinants of growth rates in OECD countries