Panel Data Methods for High-Dimensional and Machine Learning–Augmented Econometric Models

This PhD project explores how modern machine learning techniques can be integrated with panel data econometrics to handle high‑dimensional datasets, model selection and nonlinearities, while still allowing for valid statistical inference.

The motivation comes from the rapid growth of large panel datasets - from firm‑level to financial and macroeconomic panels - where traditional econometric methods can struggle with dimensionality and complex dependence structures.

Key research directions could include:

  • Developing methods that combine factor models or interactive fixed effects with machine learning tools (e.g., regularization, tree‑based methods)
  • Studying post‑selection inference in panel settings with cross‑sectional dependence
  • Extending panel data frameworks to allow for nonlinear and heterogeneous treatment effects using ML approaches
  • Addressing challenges such as overfitting, interpretability, and causal identification in high‑dimensional panels

Empirical applications could draw on large‑scale datasets such as international macro panels, firm productivity data or financial markets.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self-funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Yiannis Karavias - I joined the Department of Economics and Finance in September 2023 as Professor of  Finance. Prior to this I was an Associate Professor of Financial Economics at the University of Birmingham, in the Department of Economics. There, I also served as the Director of the Group for Research in Econometrics and Data Science.  My research interests lie in the evaluation of economic policies and interventions and in developing the appropriate statistical tools that will make these evaluations possible. Methodological interests include the analysis of panel data and of randomized control trials. I am currently ranking in the top 5% of all economists in terms of publications in the last 10 years, according to IDEAS. My research has been published in highly ranked academic journals i.e. Journal of Business and Economic Statistics, Journal of Applied Econometrics, Journal of Empirical Finance, Journal of Time Series Analysis, Journal of Financial Stability, Econometric Reviews, Computational Statistics and Data Analysis, Scandinavian Journal of Statistics etc. I am a Co-Editor at Nature's Humanities and Social Sciences Communications. In 2025, two of my scholarly articles were recognized in The Stata Journal as the most cited papers over the preceding three-year period, securing the first and third positions in the journal's citation rankings. My PhD students have successfully secured positions at prestigious institutions internationally, spanning both private and public sectors. Notable placements include: Tsinghua University, University of Essex, Bank Indonesia, Aluminum Corporation of China Limited (Chalco) and Kaplan Inc.  I have secured more than 5 million of external research funding for work on the evaluation of interventions targeted at reducing youth violence and domestic violence. These include a Home Office three-year research grant award of £1,078,377 on “What works Fund, Preventing Violence Against Women and Girls and Supporting Children”, and the "REMEDI - Restorative Services" grant award of 1,580,080 funded by the Youth Endowment Fund. My policy work is being regularly cited by major media outlets such as The Guardian and by policy makers and general and local government officials, and also in government strategy.