This paper studies a new class of semiparametric dynamic panel data models, in which some of the coefficients are allowed to depend on other informative variables and some of the regressors can be endogenous. To estimate both parametric and nonparametric coefficients, a three-stage estimation method is proposed. A nonparametric GMM is adopted to estimate all coefficients firstly and an average method is used to obtain the root-N consistent estimator of parametric coefficients. At the last stage, the estimator of varying coefficients is obtained by plugging the parametric estimator into the model. The consistency and asymptotic normality of both estimators are derived. Monte Carlo simulations verify the theoretical results and demonstrate that our estimators work well even in a finite sample.
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