This theme is concerned with the application of statistical, econometric and machine learning methods to problems arising in economics, finance and business.
|Jim Griffin (Theme Lead)||Bayesian models; Nonparametric modelling of univariate multivariate time series; macroeconomic forecasting; quantile time series modelling; forecasting with high-dimensional regression models|
|Gianluca Baio||Bayesian analysis; health economic evaluation; observational data|
|Petros Dellaportas||Bayesian econometrics, Big data and machine learning in finance.|
|Serge Guillas||Uncertainty quantification of natural hazards for (re)-insurance catastrophe modelling.|
|Ioanna Manolopoulou||Retail analytics; Sales forecasting; Market baskets; Product competition; Bayesian modelling; Count processes; Topic models.|
|Giampiero Marra||Medical Care Usage; Adverse Selection; Insurance; Copula regression; CO2 Emission Modelling|
|Afzal Siddiqui||Energy economics; Operational research; Decision making under uncertainty; Game theory|
Current and Recent Projects
• Bayesian computations for Value of Information measures using Gaussian processes, INLA and Moment Matching (PI: Baio)
• Bayesian non-parametric modelling to understand product competition (PI: Manolopoulou)
• DAMS 2.0 Design and Assessment of Resilient and Sustainable Interventions in Water-Energy-Food-Environment Mega-Systems (PI: Siddiqui)
• Detecting Anomalies in Networks: The Case of VAT (Turing HSBC Economic Data Science Project) (PI: Dellaportas): (Linked text): Revenue from VAT is collected gradually through the chain of production and distribution. This structure, or network, of interconnected businesses presents opportunities for misreporting and fraud. This project aims to develop methodologies that could be used to inform tax administrations of transactions which merit investigation.
• EnRiMa Energy Efficiency and Risk Management in Public Buildings (PI: Siddiqui)
• Forecasting with Large Macroeconomic and Financial datasets in the Presence of Structural change (Turing HSBC Economic Data Science Project) (PI: Dellaportas): (Linked text): Forecasting of economic and financial variables is crucial for decision-making by central banks, monetary authorities, financial institutions, policy makers and international economic organisations. Many of these forecasts use 'vector autoregressive (VAR)' models, that tend to do poorly when faced with volatile changes over time, for example like those caused by Brexit. This project is aiming to use state-of-the-art computational techniques to improve these models and produce freely available software to implement these improvements.
• Hawkes processes to model slow-moving goods (PI: Manolopoulou)
• Health economic evaluation of HPV vaccination (PI: Baio)
• An Options Approach to U.K. Energy Futures (PI: Siddiqui)
• PlanFES Advanced Analytics for Planning Future Energy Systems (PI: Siddiqui)
• The regression discontinuity design in epidemiology (PI: Baio)
• STRIDES Strategic Transmission and Renewable Investment in a Decentralized Electricity Sector (PI: Siddiqui)
• Topic models for market baskets (PI: Manolopoulou)