1. Partially Linear Functional-Coefficient Dynamic Panel Data Models: Sieve Estimation and Specification Testing (with Yonghui Zhang), available upon request Abstract: In this paper, we study the nonparametric estimation and testing for the partially linear functional-coefficient dynamic panel data models where the effects of some covariates on the dependent variable vary according to a set of low-dimensional variables nanparametrically. Based on the sieve approximation of unknown functions, we propose a sieve 2SLS procedure to estimate the model. The asymptotic properties for both parametric and nonparametric components are established when sample size N and T tend to infinity jointly or only N goes to infinity. We also propose a specification test for the constancy of slopes, and we show that after being appropriately standardized, our test is asymptotically normally distributed under the null hypothesis. Monte Carlo simulations show that our sieve 2SLS estimators and test perform remarkably well in finite samples. We apply our method to study the effect of income on democracy and find strong evidence of nonconstant effect of income on democracy.