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
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 (shixuan.wang@reading.ac.uk ).
Publications
Econometrics
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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, Dynamic network quantile regression model
Chen, Jia, Shin, Yongcheol, Zheng, Chaowen, Estimation and inference in heterogeneous spatial panels with a multifactor error structure
Horváth, Lajos, Liu, Zhenya, Rice, Gregory, Wang, Shixuan, Testing stability in functional event observations with an application to IPO performance
Kastoryano, Stephen, Dynamic evaluation of job search assistance Horváth, Lajos, Kokoszka, Piotr, Wang, Shixuan, Monitoring for a change point in a sequence of distributions
Horváth, Lajos, Liu, Zhenya, Rice, Gregory, Wang, Shixuan, Sequential monitoring for changes from stationarity to mild non-stationarity
Antoch, Jaromír, Hanousek, Jan, Horváth, Lajos, Hušková, Marie, Wang, Shixuan, Structural breaks in panel data: large number of panels and short length time series
Forecasting
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Wang, Shixuan, Improving automotive garage operations by categorical forecasts using a large number of variables Rostami-Tabar, Bahman, Goltsos, Thanos E., Wang, Shixuan, Forecasting for lead-time period by temporal aggregation: whether to combine and how
Angelini, Giovanni, De Angelis, Luca, Singleton, Carl, Informational efficiency and behaviour within in-play prediction markets
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, When are prediction market prices most informative?
Reade, J. James, Polls to probabilities: comparing prediction markets and opinion polls
Text Analysis
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Pan, Wei-Fong, Reade, James, Wang, Shixuan, Measuring US regional economic uncertainty
Clements, Mike P. , Reade, James J., Forecasting and forecast narratives: the Bank of England inflation reports Brown, Alasdair, Rambacussing, Dooruj, Reade, J. James, Forecasting with social media: evidence from Tweets on soccer matches
Indicator Saturation
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Apergis, Nicholas, Pan, Wei-Fong, Reade, James, Wang, Shixuan, Modelling Australian electricity prices using indicator saturation
Pretis, Felix, Reade, James, Automated General-to-Specific (GETS) regression modeling and indicator saturation methods for the detection of outliers and structural breaks
Functional Data Analysis
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Clements, Mike P., & Wang, Shixuan. (2023). Do Professional Forecasters’ Phillips Curves Incorporate the Beliefs of Others?
Horváth, Lajos, Kokoszka, Piotr, VanderDoes, Jeremy, Wang, Shixuan, Inference in functional factor models with applications to yield curves
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, A functional time series analysis of forward curves derived from commodity futures
Vine Copula
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Li, Hemei, Liu, Zhenya, Wang, Shixuan, Vines climbing higher: risk management for commodity futures markets using a regular vine copula approach
Han, Xuyuan, Liu, Zhenya, Wang, Shixuan, An R-vine copula analysis of non-ferrous metal futures with application in Value-at-Risk forecasting
Apergis, Nicholas, Gozgor, Giray, Lau, Chi Keung Marco, Wang, Shixuan, Dependence structure in the Australian electricity markets: new evidence from regular vine copulae
Events
[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 ).
Research Led Teaching
