Journal Articles: 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 Letters141, 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, 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 Econometrics200, 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. To Pool or Not to Pool: Revisited, (with M.Hashem Pesaran), Oxford Bulletin of Economics and Statistics 80, 185-217, 2018. 10. Estimation of Time-invariant Effects in Static Panel Data Models (with M.Hashem Pesaran), Econometric Reviews 37, 1137-1171, 2018. 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. 11. Binary Choice Model with Interactive Effects (with Sen Xue and Tao Yang), Economic Modelling70, 338-350, 2018. 12. Incidental Parameters, Initial Conditions and Sample Size in Statistical Inference for Dynamic Panel Data Models (with Cheng Hsiao), Journal of Econometrics207, 114-128, 2018. 13. JIVE for Panel Dynamic Simultaneous Equations Models (with Cheng Hsiao), Econometric Theory 34, 1325-1369, 2018. 14. Estimation for Time-invariant Effects in Dynamic Panel Models with Application to Income Dynamics (with Yonghui Zhang), Econometrics and Statistics 9, 62-77, 2019. 15. Panel Parametric, Semi-parametric and Nonparametric Construction of Counterfactuals (with Cheng Hsiao), Journal of Applied Econometrics34, 463-481, 2019. 16. Identification and Estimation in Panel Models with Overspecified Number of Groups (with Ruiqi Liu, Zuofeng Shang and Yonghui Zhang), Journal of Econometrics 215, 574-590, 2020. 17. Fitting partially linear functional-coefficient panel-data models with Stata (With Kerui Du and Yonghui Zhang), Stata Journal 20(4), 976-998, 2020. 18. Correction for the Asymptotical Bias of the Arellano-Bond type GMM Estimation of Dynamic Panel Models (with Yonghui Zhang), Advances in Econometrics Volume 41, 1-24, Essays in honor of Cheng Hsiao, 2020. 19. Can a Time-Varying Structure Provide a More Robust Panel Construction of Counterfactuals-Straitjacket or Straitjackets? (with Shui Ki Wan and Cheng Hsiao), Empirical Economics 60, 113-129, 2021. 20. Factor Dimension Determination for Panel Interactive Effects Models (with Cheng Hsiao and Yimeng Xie), Computational Statistics 36, 1481-1497, 2021. 21. Partially Linear Functional-Coefficient Dynamic Panel Data Models: Sieve Estimation and Specification Testing (with Yonghui Zhang), Econometric Reviews 40, 983-1006, 2021. Online Appendix 22. 2SLS and IV Estimation of Dynamic Panel Models with Heterogeneous Trend (with Shiyun Cao, Yonghui Zhang), Oxford Bulletin of Economics and Statistics 83, 1408-1431, 2021. 23. Multiple Treatment Effects in Panel-Heterogeneity and Aggregation (with Cheng Hsiao and Yan Shen), Advances in Econometrics Volume 43B, 81-101, Essays in honor of Hashem Pesaran, 2022. 24. Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks (with Yue Qiu, Tian Xie and Jun Yu), Accepted for publication atJournal of Financial Econometrics. 25. Transformed Estimator for Panel Interactive Effects Models (with Cheng Hsiao and Zhentao Shi), Accepted for publication at Journal of Business & Economic Statistics.
Working Papers 1. Specification Tests for Time-Varying Coefficient Panel Data Models (with Alev Atak and Yonghui Zhang) 2. Estimation and Inference of Treatment Effects using a New Panel Data Approach: Measuring the Impact of US SYG Law (with Huayan Geng) 3. Estimation of Dynamic Panel Data Models with Interactive Effects: Quasi-differencing over Time or Pairwise? (with Cheng Hsiao) 4. Statistical Inference for the Low Dimensional Parameters in the Presence of High-Dimensional Data: An Orthogonal Projection (with Cheng Hsiao) Abstract: We consider the estimation and statistical inference for low dimensional parameters for a model with many covariates. Instead of using usual machine learning/big data approach to select important covariates, we suggest a computationally simple orthogonal projection approach that has the advantage that (i) bypass the steps of selecting relevant important control variables, hence avoid selection mistakes, and (ii) is easy to conduct statistical inference. Monte Carlo simulations are conducted to investigate the finite sample performance of the proposed estimator, and the double/debiased estimator of Belloni et. al (2014) and Chernozhukov et. al (2018). 5. Semiparametric Least Squares Estimation of Binary Choice Panel Data Models with Endogeneity (with Anastasia Semykina, Yimeng Xie and Cynthia Fan Yang) Abstract: In this paper, we consider a semiparametric least squares estimator of binary response panel data models with endogenous regressors. The estimator relies on the correlated random effects model and control function approach to address the endogeneity due to the presence of the unobserved time-constant effect and nonzero correlation of the idiosyncratic error with one or more explanatory variables. We derive the asymptotic properties of the proposed estimator and use Monte Carlo simulations to show that it performs well in finite samples. As an illustration, the considered method is used for estimating the effect of non-wife income on labor force participation of married women. 6. A One Covariate at a Time Multiple Testing Approach to Variable Selection in Additive Models (with Liangjun Su, Thomas Tao Yang and Yonghui Zhang) Abstract: This paper proposes a one covariate at a time multiple testing (OCMT) approach to choose significant variables in high dimensional nonparametric additive regression models. Like Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive component one at a time and take into account the multiple testing nature of the problem. Both one-stage and multiple-stage procedures are considered. The former works well in terms of true positive rate (TPR) only if the marginal effects of all signals are strong enough; the latter helps to pick up the hidden signals whose marginal effects are weak. Simulations demonstrate the good finite sample performance of the proposed procedures. As an empirical application, we apply the OCMT procedure to a dataset extracted from the longitudinal survey on Rural Urban Migration in China (RUMiC). Compared with its competing methods, our procedure works well in terms of the out-of-sample forecast root mean square errors. 7. Confidence Intervals of Treatment Effects Estimation in Panel Data Models with Interactive Fixed Effects (With Xingyu Li and Yan Shen) Abstract: We consider the construction of confidence intervals for treatment effects estimated in panel models with interactive fixed effects. We use the factor-based matrix completion technique proposed by Bai and Ng (2021) to estimate the treatment effects, and use bootstrap method to construct confidence intervals of the treatment effects for treated units at each post-treatment period. Our construction of confidence intervals requires neither specific distributional assumptions on the error terms nor large number of post-treatment periods. We establish the validity of proposed bootstrap procedure that these confidence intervals have asymptotically correct coverage probabilities. Simulation studies show that these confidence intervals have satisfactory finite sample performances, and empirical applications using classical datasets yield treatment effect estimates of similar magnitude and reliable confidence intervals.