This paper considers kernel-based nonparametric estimation of panel models using local linear least squares, when both the fixed individual effects and the time effects present. The marginal effect is of the main interest. A within-group type nonparametric estimator is developed, where the...

In this paper, we consider a class of time-varying panel data models with individual-specific regression coefficients and common factors where both the serial correlation and cross-sectional dependence among error terms can be present. Based on an initial estimator of factors, we propose a...

This paper develops linear estimators for structural and causal parameters in nonparametric,nonseparable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of...

This chapter surveys nonparametric methods for estimation and inference in a panel data setting. Methods surveyed include profile likelihood, kernel smoothers, as well as series and sieve estimators. The practical application of nonparametric panel-based techniques is less prevalent that, say,...

This paper considers nonparametric estimation of autoregressive panel models with fixed effects. A within-group type series estimator is developed and its convergence rate and the asymptotic normality are derived. It is found that the series estimator is asymptotically biased and the bias...

This paper provides a constructive argument for identification of nonparametric panel data models with measurement error in a continuous explanatory variable. The approach point identifies all structural elements of the model using only observations of the outcome and the mismeasured explanatory...