This chapter develops methods for estimation and inference in nonparametric panel data models with correlated random-effects. Using the Mundlak specification to control for unobserved heterogeneity, this nonparametric estimation procedure can identify both the nonparametric function and a finite-dimensional parameter associated with (potentially) observed time-invariant regressors. We develop the necessary asymptotic theory for our proposed estimator. To assess the validity of our method in practice, we propose a consistent specification test for whether the model controls for the correlation between the unobserved individual effects and the regressors. Monte Carlo simulations support the asymptotic developments. We illustrate the practical utility of our approach via an empirical application.