About

Shu Shen is a Professor of Economics and the Graduate Program Chair at the University of California, Davis. He holds a Ph.D. and an M.S. in Economics from the University of Texas at Austin, obtained in 2011 and 2008 respectively, and a B.A. in Economics from Fudan University, Shanghai, earned in 2006. His expertise encompasses econometric theory, applied econometrics, and applied microeconometrics. Shen's research primarily focuses on econometric methods, including Regression Discontinuity, GMM Identification, Multiple Testing, Treatment Effect Heterogeneity Analysis, and Nonparametric/Semiparametric Distributional/Quantile Analysis. His scholarly work has been published in reputable journals such as the Journal of Econometrics, Econometric Journal, Review of Economics & Statistics, and the Journal of Business & Economic Statistics. He has contributed to the development of inference methods for instrumental variables regressions, treatment effect heterogeneity testing, and distributional effects estimation. In addition to his research, Shu Shen teaches courses in analysis of economic data, econometrics, and economic and financial forecasting for undergraduates, as well as cross-sectional econometrics and topics in econometrics for doctoral students. His academic contributions have been recognized through awards including the Hellman Fellow at UC Davis, an individual research grant from the UC Davis Institute for Social Sciences, and the Hale Fellowship at the University of Texas at Austin.

Research topics

• Econometrics
• Mathematics
• Economics
• Statistics
• Computer Science
• Political Science
• Geology
• Environmental science
• Geography
• Meteorology
• Medicine

Selected publications

• Grauert's direct image theorem via superconnections and desingularizations

  ArXiv.org · 2025-11-15

  preprintOpen access1st authorCorresponding

  We give a new differential-geometric proof of Grauert's theorem on the coherence of the higher direct image of a coherent sheaf under a proper holomorphic morphism between complex analytic spaces. In the smooth case, our approach is based on the antiholomorphic superconnection introduced by Block and further developed by Bismut-Shen-Wei. The required finiteness results follow from elliptic theory. In the singular case, we reduce the problem to the smooth setting using Hironaka's desingularization.

  PublisherOA PDFDOI

• Heat kernel, large-time behavior, and representation theory

  ArXiv.org · 2025-03-01

  preprintOpen access1st authorCorresponding

  Given a real reductive group $G$, the purpose of this paper is to show an asymptotic formula of the large-time behavior of the $G$-trace of the heat operator on the associated symmetric spaces. Together with Carmona's proof on Vogan's lambda map, our results provide a geometric counterpart of Vogan's minimal $K$-type theory.

  PublisherOA PDFDOI

• Instrumental Variable Regression with Varying-Intensity Repeated Treatments

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Dynamic regression discontinuity under treatment effect heterogeneity

  Quantitative Economics · 2024-01-01 · 7 citations

  articleOpen accessSenior author

  Regression discontinuity is a popular tool for analyzing economic policies or treatment interventions. This research extends the classic static RD model to a dynamic framework, where observations are eligible for repeated RD events and, therefore, treatments. Such dynamics often complicate the identification and estimation of long‐term average treatment effects. Empirical papers with such designs have so far ignored the dynamics or adopted restrictive identifying assumptions. This paper presents identification strategies under various sets of weaker identifying assumptions and proposes associated estimation and inference methods. The proposed methods are applied to revisit the seminal study of Cellini, Ferreira, and Rothstein (2010) on long‐term effects of California local school bonds.

  PublisherDOI

• Instrumental variable estimation with first-stage heterogeneity

  Journal of Econometrics · 2023 · 33 citations

  Senior authorCorresponding
  • Econometrics
  • Mathematics
  • Statistics
  PublisherDOI

• A Type of Recharging Scheduling Strategy based on Adjustable Request Threshold in WRSNs

  2023-11-02 · 1 citations

  article

  In order to utilize the advantages of the “Periodic Energy Replenishing” as well as the “Request Triggered Recharging” methods in Wireless Rechargeable Sensor Networks, a type of Recharging Scheduling Strategy based on Adjustable Request Threshold (RSS-ART) is proposed in this paper. The Double Recharging Request Thresholds (DRRTs) are set for each node to let them send out their requests in advance or later. In this way, the MCV is more likely to recharge nodes in urgent need of energy replenishment which enhances its energy efficiency. Moreover, to maximize the surviving rate of nodes and to balance the number of requests in each time period, we limit the traverse duration of each round for the MCV. Finally, the departure time of each round of traversal is calculated out with the help of the number of two kinds of requests (WRR and DRR) which enhances the flexibility for MCV to perform charging tasks. Experiments show that RSS-ART outperforms the compared methods in term of the surviving rate and energy efficiency of MCV with different network scales.

  PublisherDOI

• Inference on optimal treatment assignments

  Japanese Economic Review · 2023-09-27 · 5 citations

  articleOpen accessSenior author

  Abstract We consider inference on optimal treatment assignments. Our methods allow inference on the treatment assignment rule that would be optimal given knowledge of the population treatment effect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the population for which assignment to treatment can be determined to be optimal after conditioning on all available information, with a prespecified level of confidence. A Monte Carlo study confirms that the inference procedure has good small sample behavior. We apply the method to study Project STAR and the optimal assignment of a small class intervention based on school and teacher characteristics.

  PublisherOA PDFDOI

• Figure4CTable2.do

  Harvard Dataverse · 2020-01-01

  datasetOpen access

  :unav

  PublisherDOI

• Replication Data for: A Censored Maximum Likelihood Approach to Quantifying Manipulation in China's Air Pollution Data

  Harvard Dataverse · 2020-04-22

  datasetOpen access

  The datasets and codes are supporting the project of identifying threshold manipulation in the Chinese air pollution data through a censored maximum likelihood approach.

  PublisherDOI

• PM10CMLE_withinference.do

  Harvard Dataverse · 2020-01-01

  datasetOpen access

  :unav

  PublisherDOI

Frequent coauthors

• Dalia Ghanem

  45 shared

• Junjie Zhang

  State Grid Corporation of China (China)

  45 shared

• Yingying Dong

  23 shared

• Zhiqiang Zou

  Tsinghua University

  17 shared

• Yu‐Chin Hsu

  9 shared

• Ruchuan Wang

  Nanjing University of Posts and Telecommunications

  9 shared

• Vittorio Camarchia

  Polytechnic University of Turin

  6 shared

• Ai Xia Gu

  Hebei Agricultural University

  5 shared

Awards & honors

• Hellman Fellow, University of California, Davis, 2015
• Individual Research Grant, UC Davis Institute for Social Sci…
• Small research grant, University of California, Davis, 2012–…
• Hale Fellowship, University of Texas at Austin, 2008