Chairs of Statistics and Econometrics

Shu Wang

Research Interests

    I am a postdoctoral researcher in econometrics. I completed my Ph.D. in May 2023 with a dissertation on modern methods for causal identification and statistical inference in structural vector autoregressions. My research focuses on the structural identification of dynamic simultaneous equation models, with a particular emphasis on developing econometric methods to analyze the transmission mechanisms of structural shocks in multivariate time series systems. I am also broadly interested in time series analysis, including models with volatility clustering and spillovers, time-varying parameters, and the econometric treatment of integration and cointegration. My current work spans both methodological and applied research in macroeconometrics and financial econometrics, with applications to topics such as monetary policy, oil price shocks, and climate-related dynamics.

Working Papers

  • Daily oil price shocks and their uncertainties Link
  • Geometric ergodicity and mixing of a class of multivariate GARCH models with regime-switching innovations (with H. Herwartz)
  • Accounting for financial stress in Taylor‐rule based estimates of the natural rate of interest (with B. Kempa and F. Zou)
  • Markov-switching multivariate GARCH model with copula-distributed innovations (with M. Fülle, H. Herwartz) Link
  • Identification of independent shocks under (co-)heteroskedasticity (with H. Herwartz) Link
  • Sign restrictions with independence ranks (with M. Ademmer and H. Herwartz)
  • Transmission of shocks in a unified monetary and financial framework: Homogeneity, asymmetry and structural change in the Euro area (with H. Herwartz)

Publications

  • Keweloh, S. and Wang, S. (2025): Uncertain short‐run restrictions and statistically identified structural vector autoregressions, Journal of Applied Econometrics, forthcoming.
  • Bui, H. T. D., Herwartz, H. and Wang, S. (2025): Central bank announcements and monitoring portfolio risks. International Review of Economics and Finance, forthcoming.
  • Keweloh, S. and Wang, S. (2025): Higher moments and efficiency gains in recursive structural vector autoregressions, Oxford Bulletin of Economics and Statistics, forthcoming.
  • Hafner, C., Herwartz, H. and Wang, S. (2025): Statistical identification of independent shocks with kernel-based maximum likelihood estimation and an application to the global crude oil market, Journal of Business & Economic Statistics, forthcoming. Link
  • Herwartz, H. and Wang, S. (2024): Statistical identification in panel structural vector autoregressive models based on independence criteria, Journal of Applied Econometrics, 39(4), 620–639. Link
  • Herwartz, H., Theilen, B. and Wang, S. (2024): Unraveling the structural sources of oil production and their impact on CO2 emissions, Energy Economics, 132, 107488. Link
  • Herwartz, H. and Wang, S. (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 Wang, S. (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

  • Structural volatility spillovers
  • Carbon productivity
  • Dynamic impacts of oil shocks
  • Transmission of uncertainty
  • 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

Teaching

  • Summer/Winter (since 2024) : Introduction to Statistical Methods in Economic Sciences (Graduate, Lecture) Lecture Notes
  • Winter (since 2023): Multivariate Time Series Analysis (Graduate, Lecture)
  • Summer term (2021 - 2023): Introduction to Time Series Analysis (Graduate, Exercise)
  • Winter term (2020): Applied Econometrics (Graduate, Exercise)