Econometrics with Data Science


As we enter the big data era, there is a boom in the development of modern techniques, such as high-dimensional econometrics, approaches for non-Euclidean data structures, and machine learning methods. The research cluster of Econometrics with Data Science (EwDS) embraces this trend and brings together researchers with expertise in theoretical econometrics, applied econometrics, forecasting, and various data science techniques, including text analysis, indicator saturation, functional data analysis, and vine copula.

The cluster members have published their research in the world-leading academic journals: Annals of Statistics, Journal of Econometrics, Journal of Business and Economic Statistics, European Journal of Operational Research, Journal of Applied Econometrics, Journal of Financial Econometrics, Econometric Review, and International Journal of Forecasting, among others.

Our Members

Academics at Reading:

Doctoral Researchers:

  • Hamed Alaidarous
  • Omar Alarfaj
  • Baker Audeh
  • Albert Chongo
  • Minko Markovski
  • Lillian Mookodi
  • Winnie Muangi
  • Okiemua Okoror
  • Stephen Opata
  • Jingqi Pan
  • Philip Ramirez
  • Hafsa Shoukat
  • Yi Sun
  • Elly Twineyo

We are a newly established cluster (est. March 2023) and welcome new members (from any department at the University of Reading) whose research involves econometrics and data science. If you are interested in joining us, please contact the cluster coordinator, Shixuan Wang (




Text Analysis

Indicator Saturation

Functional Data Analysis

Vine Copula



  • [Online Workshop] How to use ChatGPT to facilitate academic research? – Yuhao Mu (Renmin University of China), April 19, 12 noon via Teams. Yuhao’s slides are here. Video recording below:

If you would like to receive information about our future events, please contact the cluster coordinator, Shixuan Wang (

Research Led Teaching