Shu Wang

Research Interests

    I defended my dissertation on modern approaches to causal inference in structural vector autoregressions in May 2023 and have been working as a Postdoctoral Associate at the Chair since June 2023. My research specializes in identification of structural dynamic simultaneous equations models. I am dedicated to advancing econometric methods to investigate transmission mechanisms of structural shocks in multivariate systems and study complex causal relationships among the observables. Additionally, my research interests encompass other topics in time series analysis, including stochastic volatilities, time-varying parameters, integrations, and co-integrations.

Working Papers

  • Consistent statistical identification of SVARs under (co-)heteroskedasticity of unknown form (with H. Herwartz) Link
  • Causal inference with (partially) independent shocks and structural signals on the global crude oil market (with C. Hafner and H. Herwartz) Link
  • Transmission of shocks in a unified monetary and financial framework: Homogeneity, asymmetry and structural change in the Euro area (with H. Herwartz)


  • Herwartz, H. and S. Wang (2024): Statistical identification in panel structural vector autoregressive models based on independence criteria, Journal of Applied Econometrics, forthcoming.
  • Herwartz, H. and S. Wang (2023): Point estimation in sign-restricted SVARs based on independence criteria with an application to rational bubbles, Journal of Economic Dynamics and Control, 151, 104630. Link
  • Herwartz, H., H. Rohloff, and S. Wang (2022): Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US, Journal of Economic Dynamics and Control, 139, 104457. Link

Work in Progress

  • Identification of independent shocks with unstable volatility
  • Robust methods for blind source separation
  • Monetary policy transmission in economies with heterogeneous households
  • Time-varying Taylor rule


  • Spring 2024: Introduction to Statistical Methods in Economic Sciences (Master)
  • Fall 2023: Multivariate Time Series Analysis (Master)
  • Spring 2021/2022/2023: Introduction to Time Series Analysis (Master)
  • Fall 2020: Applied Econometrics (Master)