Econometrics with Data Science

Academics at Reading:

Nico Biagi (Business Informatics, Systems & Accounting)
Michael Clements (ICMA Centre)
Hussein Hassan (Economics)
Stephen Kastoryano (Economics)
Emese Lazar (ICMA Centre)
Stefania Lovo (Economics)
Abhishek Pal Majumder (Mathematics and Statistics)
James Reade (Economics)
Shixuan Wang (Economics) – Lead
Fangya Xu (Economics)
Yuan Zhao (Real Estate and Planning)

Doctoral Researchers:

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

Visiting Member:

Michael Kunkler

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/or data science. If you are interested in joining us, please contact the cluster coordinator, Shixuan Wang (shixuan.wang@reading.ac.uk).

Econometrics

Chen, Jian, Clements, Michael P., Urquhart, Andrew, (2023) Modelling price and variance jump clustering using the marked Hawkes process. Journal of Financial Econometrics.
Lazar, Emese, Wang, Shixuan, Xue, Xiaohan, (2023) Loss function-based change point detection in risk measures. European Journal of Operational Research.
Xu, Xiu, Wang, Weining, Shin, Yongcheol, Zheng, Chaowen, (2022) Dynamic network quantile regression model. Journal of Business and Economic Statistics.
Chen, Jia, Shin, Yongcheol, Zheng, Chaowen, (2022) Estimation and inference in heterogeneous spatial panels with a multifactor error structure. Journal of Econometrics, 229 (1). pp. 55-79.
Horváth, Lajos, Liu, Zhenya, Rice, Gregory, Wang, Shixuan, Zhan, Yaosong, (2022) Testing stability in functional event observations with an application to IPO performance. Journal of Business and Economic Statistics.
Kastoryano, Stephen, van der Klaauw, Bas, (2021) Dynamic evaluation of job search assistance. Journal of Applied Econometrics, 37(2). pp. 227-241.
Horváth, Lajos, Kokoszka, Piotr, Wang, Shixuan, (2021) Monitoring for a change point in a sequence of distributions. Annals of Statistics, 49 (4). pp. 2271-2291.
Horváth, Lajos, Liu, Zhenya, Rice, Gregory, Wang, Shixuan, (2020) Sequential monitoring for changes from stationarity to mild non-stationarity. Journal of Econometrics, 215 (1). pp. 209-238.
Antoch, Jaromír, Hanousek, Jan, Horváth, Lajos, Hušková, Marie, Wang, Shixuan, (2019) Structural breaks in panel data: large number of panels and short length time series. Econometric Reviews, 38(7). pp. 828-855.

Forecasting

Wang, Shixuan, Syntetos, Aris A., Liu, Ying, Di Cairano-Gilfedder, Carla, Naim, Mohamed M., (2023) Improving automotive garage operations by categorical forecasts using a large number of variables. European Journal of Operational Research, 306 (2). pp. 893-908.
Rostami-Tabar, Bahman, Goltsos, Thanos E., Wang, Shixuan, (2023) Forecasting for lead-time period by temporal aggregation: whether to combine and how. Computers in Industry, 145.
Ramirez, Philip, Reade, J. James, Singleton, Carl, (2022) Betting on a buzz: mispricing and inefficiency in online sportsbooks. International Journal of Forecasting.
Brown, Alasdair, Reade, J. James, Vaughan Williams, Leighton, (2019) When are prediction market prices most informative?. International Journal of Forecasting, 35 (1). pp. 420-428.
Reade, J. James, Vaughan Williams, Leighton, (2019) Polls to probabilities: comparing prediction markets and opinion polls. International Journal of Forecasting, 35 (1). pp. 336-350.

Text Analysis

Pan, Wei-Fong, Reade, James, Wang, Shixuan, (2022) Measuring US regional economic uncertainty. Journal of Regional Science, 62 (4). pp. 1149-1178.
Clements, Mike P., Reade, James J., (2020) Forecasting and forecast narratives: the Bank of England inflation reports. International Journal of Forecasting, 36 (4). pp. 1488-1500.
Brown, Alasdair, Rambacussing, Dooruj, Reade, J. James, Rossi, Giambattista, (2018) Forecasting with social media: evidence from Tweets on soccer matches. Economic Inquiry, 56 (3). pp. 1748-1763.

Indicator Saturation

Apergis, Nicholas, Pan, Wei-Fong, Reade, James, Wang, Shixuan, (2023) Modelling Australian electricity prices using indicator saturation. Energy Economics, 120.
Pretis, Felix, Reade, James, Sucarrat, Genaro, (2018) Automated General-to-Specific (GETS) regression modeling and indicator saturation methods for the detection of outliers and structural breaks. Journal of Statistical Software, 86 (3).

Functional Data Analysis

Clements, Mike P., & Wang, Shixuan. (2023). Do Professional Forecasters’ Phillips Curves Incorporate the Beliefs of Others? Economics Discussion Paper, University of Reading.
Horváth, Lajos, Kokoszka, Piotr, VanderDoes, Jeremy, Wang, Shixuan, (2022) Inference in functional factor models with applications to yield curves. Journal of Time Series Analysis, 43 (6). pp. 872-894.
Bouri, Elie, Lau, Chi Keung Macro, Saeed, Tareq, Wang, Shixuan, Zhao, Yuqian, (2021) On the intraday return curves of Bitcoin: predictability and trading opportunities. International Review of Financial Analysis, 76.
Horváth, Lajos, Liu, Zhenya, Rice, Gregory, Wang, Shixuan, (2020) A functional time series analysis of forward curves derived from commodity futures. International Journal of Forecasting, 36 (2). pp. 646-665.

Improving Staff Retention at the RBFT: Econometric Data Analytics, Probabilistic Forecasting and Management Intelligence

(Team: Pete Sandham, People Directorate, RBFT; Shixuan Wang, Department of Economics, UoR; Rita Fontinha, Henley Business School; Eghosa Bazuaye, RBFT; Arran Rogers, RBFT; and Cindy Kouris, RBFT)

The team seeks to reform the Trust’s approach to staff retention at the Royal Berkshire NHS Foundation Trust (RBFT). They will research, test and deliver a solution that enables them to model and predict the likelihood of colleagues leaving the Trust in the future. Through the project, the team aims to create interventions to support workforce retention. This project is funded by Collaborative Innovation Fund.

- [In-person Conference] We are excited to announce the 1st Conference “Econometrics with Data Science” is going to take place at London Road Campus, University of Reading on 16th September 2024, in an in-person format. See the details on the conference webpage.

[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.

If you would like to receive information about our future events, please contact the cluster coordinator, Shixuan Wang (shixuan.wang@reading.ac.uk).

Our Members

Publications

Funded Projects

Improving Staff Retention at the RBFT: Econometric Data Analytics, Probabilistic Forecasting and Management Intelligence

Events

Research Led Teaching