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. 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 Econometrics, 207, 114-128. 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), Conditionally accepted for publication at Journal of Applied Econometrics.
1. Partially Linear Functional-Coefficient Dynamic Panel Data Models: Sieve Estimation and Specification Testing (with Yonghui Zhang) Stata command to implement the 2SLS sieve estimation can be found here (Credit to Kerry Du). A detailed description of the Stata command can be found here. 2. Bias correction for Arellano-Bond type GMM estimation of Dynamic Panel Models (with Yonghui Zhang) 3. Estimation of Dynamic Panel Data Models with Interactive Effects: Quasi-differencing over Time or Pairwise? (with Cheng Hsiao), Revise and Resubmit at JOE 4. Identification and estimation in panel models with overspecified number of groups (with Ruiqi Liu, Anton Schick, Zuofeng Shang and Yonghui Zhang), Revise and Resubmit at JOE 5. GMM and IV Estimation of Dynamic Panel Models with Heterogeneous Trend (with Niansheng Tang, Shiyun Cao, Yonghui Zhang) 6. Specification Tests for Time-Varying Coefficient Panel Data Models (with Alev Atak and Yonghui Zhang) (coming soon) Abstract: This paper provides a nonparametric test for the most commonly-used structure in panels, i.e., the homogeneity and stability on parameters. We first obtain the augmented residuals by estimating the model under the null hypothesis of homogeneity and stability, then we run auxiliary time series regressions of the augmented residuals on the regressors with time-varying coefficients via the sieve method. Our proposed test statistic is constructed by averaging all the squared fitted values, which is close to zero under the null and deviates from zero under the alternative. We show that the testing statistic, after being appropriately standardized, is asymptotically normally distributed under the null and a sequence of Pitman local alternatives as both cross-sectional and time dimensions tend to infinity. A bootstrap procedure is proposed to improve the finite sample performance of the test. Monte Carlo simulations indicate that the proposed test performs reasonably well in finite samples. We apply our test to environmental Kuznets Curve estimation and reject the assumption of homogeneous and stable coefficients in the model. In addition, we extend the approach to test other structures on parameters in panels such as homogeneity of time-varying coefficients or stability of heterogeneous coefficients. 7. Estimation and Inference of Treatment Effects using a New Panel Data Approach: Measuring the Impact of US SYG Law (with Huayan Geng) (coming soon) Abstract: This paper proposes a new panel data approach to measure the impact of social policy. We consider a classical panel model with interactive fixed effects (IFE), which allows the cross-sectional dependence through the presence of some (unobserved) common factors. The new approach combines the idea of Pesaran (2006) to estimate panel model with IFE and Hsiao et al. (2012) to construct counterfactuals. For the new approach, instead of estimating the unobserved factors, we propose to use observed data. Compared to the existing methods such as Synthetic Control Method (SCM) (Abadie et al. (2010)) and the Generalized SCM (GSCM) (Xu (2017)), our new approach has the advantages of: (1) there is no need to impose constraints on both observables and unobservables; (2) the number of parameters to be estimated in the model is greatly reduced. Moreover, we establish the asymptotic properties for the average treatment effect (ATE) over post-treatment periods, which can be used to obtain statistical inference for the significance of ATE or to construct confidence band for the treatment effects in the post-treatment periods. Monte Carlo simulations show that our approach works remarkably well and has very desirable finite sample performance in terms of estimation bias, mean square of errors, and empirical rejection frequency. We apply our method to study the impact of US Stand Your Ground (SYG) law on the state-level murder rate, and we find, in general, the SYG has increased the murder rate for the states adapting SYG law.
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