About

Ph.D. in Economics from Texas A&M University, 2019. Assistant professor at Georgia Tech. Research interests include Econometric Theory, Applied Econometrics, and Empirical Industrial Organization.

Research topics

• Econometrics
• Mathematics
• Economics
• Statistics
• Computer science

Selected publications

• Varying-coefficient spatial dynamic panel data models with fixed effects: Theory and application

  Journal of Econometrics · 2024-10-01 · 9 citations

  articleSenior author
  PublisherDOI

• On the estimation of quantile treatment effects using a semiparametric propensity score

  Econometric Reviews · 2024-07-09

  articleOpen accessSenior authorCorresponding

  This article considers the estimation of quantile treatment effects under the assumption of unconfoundedness given quasi-experimental data. We propose a semiparametric single-index method to estimate the propensity score. Our approach overcomes the curse of dimensionality issue of a nonparametric propensity score and can handle a moderately large dimension of covariates. It is more flexible than the parametric propensity score and thereby alleviates the possible model misspecification problem. We derive the asymptotic distribution of the quantile treatment effect estimator that is based on the semiparametric propensity score. We also propose a consistent variance estimator and construct the confidence intervals for the QTE estimator. Monte Carlo simulation results show that the proposed estimator performs well in finite samples and the confidence intervals have adequate coverage rates. We demonstrate the usefulness of our method by applying it to a study of the quantile treatment effects of college education on income.

  PublisherOA PDFDOI

• Editorial for the special issue on public policy and economic behavior: China and the world amidst a global pandemic

  China Economic Review · 2023-01-16

  editorialOpen access1st authorCorresponding
  PublisherOA PDFDOI

• How do the stay-at-home (SAH) orders affect air quality? Evidence from the northeastern USA

  Empirical Economics · 2022-10-27

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Oil supply news shock and Chinese economy

  China Economic Review · 2022-04-09 · 17 citations

  articleSenior author
  PublisherDOI

• 1104 A safe and highly potent PD-1-IL-2 fusion (AWT020) that decouples the efficacy and toxicity of IL-2 therapy

  Regular and Young Investigator Award Abstracts · 2022-11-01 · 3 citations

  articleOpen access

  <h3>Background</h3> Interleukin 2 (IL-2) is a pivotal immune agonist for tumor immunotherapy that has demonstrated its clinical efficacies in melanoma and renal cell carcinoma. Nevertheless, its pleiotropic effect has led to severe side effects and its antitumor activity is compromised by its activation of regulatory T cells. In contrast, the PD-1 blockade-based cancer immunotherapy has good safety profiles by targeting and sustaining the activity of tumor-antigen specific T cells within cancer tissues. To take advantage of the complementary antitumor activity of PD-1 monoclonal antibody (mAb) and IL-2, a bifunctional fusion protein composed of PD-1 mAb and IL-2c mutein (AWT020) is designed to enhance the therapeutic efficacy while reducing the IL-2 related toxicity (figure 1). <h3>Methods</h3> The in vitro activity of AWT020 was verified using STAT5 signaling assays and human PBMC proliferation assays. A mouse surrogate of AWT020 (mAWT020) was tested in multiple syngeneic tumor models including colon carcinoma models (MC38 and CT26), melanoma model (B16F10), and breast carcinoma model (EMT6). The tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of AWT020 were accessed in cynomolgus monkeys. <h3>Results</h3> AWT020 stimulated much greater pSTAT5 activation and proliferation in PD-1⁺ T cells than PD-1^- T cells. The high specificity of AWT020 on PD1+ T cells not only minimizes the systematic toxicity but also improved the anti-tumor efficacy. In PD-1 resistant B16F10 and EMT6 models, mAWT020 achieved &gt;90% TGI, while in CT26 tumor, mAWT020 treatment achieved 70% complete response (CR). In MC38 model, mAWT020 achieved 100% CR with a single dose at 0.3 mg/kg. Cell phenotyping studies showed that mAWT020 specifically and significantly expands tumor-infiltrating CD8⁺ T cells but has minimal effects on peripheral T cells and NK cells. Global gene expression profiling studies showed that mAWT020 significantly elevated expression levels of Cd3d, Cd3e, Cd8a, Il2rα, Cxcr3, Cxcr6, Zap70, Lck, and Pdcd1 inside tumor tissues, indicating a specific expansion and activation of T cells. Single dose study at up to 10 mg/kg in cynomolgus monkeys showed that AWT020 was well tolerated, with good exposure and long half-life. <h3>Conclusions</h3> The high target specificity of AWT020 significantly mitigates the IL-2 related adverse side effects and allows it to be dosed at a much higher level compared to IL-2 therapy, achieving full blockade of PD-1 and optimal activation of intratumoral CD8⁺ T cells. <h3>Ethics Approval</h3> The protocol of animal studies has been reviewed and approved by IACUC.

  PublisherOA PDFDOI

• A SIMPLE NONPARAMETRIC APPROACH FOR ESTIMATION AND INFERENCE OF CONDITIONAL QUANTILE FUNCTIONS

  Econometric Theory · 2021-12-13 · 3 citations

  articleOpen accessSenior author

  In this paper, we present a new nonparametric method for estimating a conditional quantile function and develop its weak convergence theory. The proposed estimator is computationally easy to implement and automatically ensures quantile monotonicity by construction. For inference, we propose to use a residual bootstrap method. Our Monte Carlo simulations show that this new estimator compares well with the check-function-based estimator in terms of estimation mean squared error. The bootstrap confidence bands yield adequate coverage probabilities. An empirical example uses a dataset of Canadian high school graduate earnings, illustrating the usefulness of the proposed method in applications.

  PublisherOA PDFDOI

• Estimation of average treatment effect based on a semiparametric propensity score

  Econometric Reviews · 2021-08-05 · 6 citations

  article

  This paper considers the estimation of average treatment effect using propensity score method. We propose to use a semiparametric single-index model to estimate the propensity score. This avoids the curse of dimensionality problem with the nonparametric method based propensity score estimator. We establish the asymptotic distribution of the average treatment effect estimator. Monte Carlo simulation results show that the proposed method works well in finite samples and outperforms the conventional nonparametric kernel approach. We apply the proposed method to an empirical data examining the efficacy of right heart catheterization on medical outcomes.

  PublisherDOI

• Kernel smoothed probability mass functions for ordered datatypes

  Journal of nonparametric statistics · 2020-05-12 · 7 citations

  articleSenior author

  We propose a kernel function for ordered categorical data that overcomes limitations present in ordered kernel functions appearing in the literature on the estimation of probability mass functions for multinomial ordered data. Some limitations arise from assumptions made about the support of the underlying random variable. Furthermore, many existing ordered kernel functions lack a particularly appealing property, namely the ability to deliver discrete uniform probability estimates for some value of the smoothing parameter. We propose an asymmetric empirical support kernel function that adapts to the data at hand and possesses certain desirable features. There are no difficulties arising from zero counts caused by gaps in the data while it encompasses both the empirical proportions and the discrete uniform probabilities at the lower and upper boundaries of the smoothing parameter. We propose likelihood and least-squares cross-validation for smoothing parameter selection and study their asymptotic and finite-sample behaviour.

  PublisherDOI

• Kernel smoothed probability mass functions for ordered datatypes

  Figshare · 2020-01-01

  preprintOpen accessSenior author

  We propose a kernel function for ordered categorical data that overcomes limitations present in ordered kernel functions appearing in the literature on the estimation of probability mass functions for multinomial ordered data. Some limitations arise from assumptions made about the support of the underlying random variable. Furthermore, many existing ordered kernel functions lack a particularly appealing property, namely the ability to deliver discrete uniform probability estimates for some value of the smoothing parameter. We propose an asymmetric <i>empirical support</i> kernel function that adapts to the data at hand and possesses certain desirable features. There are no difficulties arising from zero counts caused by gaps in the data while it encompasses both the empirical proportions and the discrete uniform probabilities at the lower and upper boundaries of the smoothing parameter. We propose likelihood and least-squares cross-validation for smoothing parameter selection and study their asymptotic and finite-sample behaviour.

  OA PDFDOI

Frequent coauthors

• Qi Li

  Hebei North University

  24 shared

• Nickolaos Tzeremes

  University of Thessaly

  16 shared

• Yiguo Sun

  University of Guelph

  16 shared

• Pantelis Kalaitzidakis

  University of Guelph

  16 shared

• Ximing Wu

  Agricultural & Applied Economics Association

  12 shared

• Theofanis P. Mamuneas

  University of Cyprus

  12 shared

• Qi Li

  9 shared

• Thanasis Stengos

  4 shared

Labs

• Karen YanPI