1. Statistical inference for panel dynamic simultaneous equations models (with Cheng Hsiao), Journal of Econometrics 189, 383–396, 2015. online appendix 2. Asymptotic theory for linear diffusion processes under alternative sampling schemes (with Jun Yu), Economics Letters 128, 1-5, 2015. 3. A Stein-like estimator for linear panel data models (with Yun Wang and Yonghui Zhang), Economics Letters 141, 156-161, 2016. 4. Asymptotic distribution of quasi-maximum likelihood estimation of dynamic panels using long difference transformation when both N and T are large (with Cheng Hsiao), Statistical Methods and Applications 25(4), 675-683, 2016. 5. Common Correlated Effects Estimation of Unbalanced Panel Data Models with Cross-Sectional Dependence (with Yonghui Zhang), Journal of Economic Theory and Econometrics 27, 25-45, 2016. 6. Panel Kink Regression with an unknown Threshold (with Yonghui Zhang and Li Jiang), Economics Letters 157, 116-121, 2017. 7. Many IVs estimation of dynamic panel regression models with measurement errors (With Nayoung Lee and Roger Moon), Journal of Econometrics 200, 251-259, 2017. 8. First Difference or Forward Demeaning: Implications for the Method of Moments Estimators (with Cheng Hsiao), Econometric Reviews 36, 883-897, 2017. 9. Estimation of time-invariant effects in static panel data models (with M.Hashem Pesaran), online supplement, Accepted for publication at Econometric Reviews Stata command to implement the FEF and FEF-IV estimators can be found here(Credit to Yui Law), help file of the command can be found here, a detailed description of the Stata command can be found here. 10. Binary choice model with interactive effects (with Sen Xue and Tao Yang), Accepted for publication at Economic Modelling. 11. JIVE for Panel Dynamic Simultaneous Equations Models (with Cheng Hsiao), Accepted for publication at Econometric Theory. 12. Estimation for time-invariant effects in dynamic panel models with application to income dynamics (with Yonghui Zhang), Accepted for publication atEconometrics and Statistics. 13. To Pool or Not to Pool: Revisited, (with M.Hashem Pesaran), Accepted for publication at Oxford Bulletin of Economics and Statistics
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.
5. 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.