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.
8. Binary choice model with interactive effects (with Sen Xue and Tao Yang), under review
9. Identification and estimation of panel data models with group structures (with Ruiqi Liu, Anton Schick, Zuofeng Shang and Yonghui Zhang) available upon request Abstract: In this paper, we provide a simple approach to identify and estimate group structure in panel models using the M-estimation. We consider both linear and nonlinear panel models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown to researchers. The main result of the paper is that under certain assumptions, our estimation and classification method is consistent even if one uses an incorrect number of groups as long as this number is not underestimated. Conditions under which estimation of groups and regression coefficients are consistent and asymptotically normal are also provided in the paper. Monte Carlo simulations are conducted to examine the finite sample properties of the M-estimation. Findings in the simulation confirm our theoretical results in the paper. Application to labor force participation also highlights the necessity to take into account of individual heterogeneity and group heterogeneity.