Econometrics with Data Science - Economics Research

Background

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
Wei Hu
Minko Markovski
Lillian Mookodi
Winnie Muangi
Okiemua Okoror
Stephen Opata
Jingqi Pan
Philip Ramirez
Hafsa Shoukat
Simeon Simeonov
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 (shixuan.wang@reading.ac.uk).

Publications

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.

Funded Projects

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.

Events

[YouTube] Being BETTER with Data Analysis – Shixuan Wang talked with Max Dorey on his YouTube channel about econometrics, data analysis and how businesses can incorporate better analytics into their operations.
[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 (shixuan.wang@reading.ac.uk).

Our Members

Publications

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Funded Projects

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

Events

Research Led Teaching

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