From Data to Decisions: The centre was established in 2025 in response to the rapid growth in the volume of economic and financial information and the need for theoretically grounded tools for its analysis. We bring together economists, statisticians, and data engineers to develop and implement methods that turn big data into clear insights for the government, banks, and business.
Our Mission is to push the boundaries of knowledge in the field of big data econometrics and to train a new generation of researchers who are equally confident in theory, programming code, and applied tasks.
Key Research Areas:
- Theory for Ultra-Large Data
High dimensionality, heterogeneity, heavy tails, and endogeneity—we derive new asymptotics and create robust estimation methods. - Non-Parametrics and AI
Smoothing splines, LASSO regularisation, tree ensembles, and deep learning models—we develop algorithms with proven convergence properties. - Applied Analytics
Macro- and micro-finance, market forecasting, risk assessment, and evaluation of government policy effectiveness—we turn models into working solutions. - Education and Community
Master's courses, summer schools, and open seminars connect learning with real-world projects and the publication culture of Top Five journals. - Partnerships and Consulting
Joint research and implementation projects with the Central Bank, leading commercial banks, IT companies, and government authorities.
News
Taisia Pimenova, Valeria Kolycheva, Alexander Semenov, and Dmitry Grigoriev have published the article «Art pricing in the emerging markets: An empirical analysis» in Emerging Markets Review . The paper presents an empirical analysis of the impact of public sentiment expressed on social media on artwork prices in emerging markets. Using a dataset of 3,282 paintings by Russian and Chinese artists, the authors demonstrate that both positive and negative public opinion significantly affect prices, while also identifying the moderating roles of collectors' investment intentions and geopolitical risk.
April 17
Martin Burda and Artem Prokhorov have published the article «Bayesian Adaptive Sparse Copula» in the Journal of Computational and Graphical Statistics. The paper introduces a new approach to Bayesian nonparametric estimation of multivariate densities. The authors propose a random Bernstein polynomial prior augmented with a spike-and-slab shrinkage structure, which preserves the advantages of multiscale decision tree methods while alleviating the curse of dimensionality.
April 15
Anastasia Gergenreter has published an article «Estimation of treatment effects on ordinal variables in multivariate ordered choice models» in Applied Econometrics . The study offers a fresh perspective on estimating treatment effects for ordinal outcomes in settings where non-random selection is present and the conditional independence assumption no longer holds.
April 08
A research team comprising Sofiia Dolgikh and Bogdan Potanin has published a paper titled “Fertility and labor supply in Mexico” in the Journal of Economic Studies. The article estimates wage elasticities of labor supply of Mexican married women with different numbers of children and analyzes the treatment effect of fertility on labor supply of these women.
March 21
A research team comprising Zheng Wei, Huiyan Sang, Artem Prokhorov, and Yu Ma has published a paper titled “Shape-Aware Deep Learning for Models of Production” in Journal of Productivity Analysis.
The study proposes a breakthrough method that combines the power of Deep Neural Networks (DNNs) with fundamental economic principles.
The study proposes a breakthrough method that combines the power of Deep Neural Networks (DNNs) with fundamental economic principles.
February 03
An international research team including Subal C. Kumbhakar, A. Peresetsky, Y. Shchetynin, and A. Zaytsev has published a paper “Technical efficiency and inefficiency: Reliability of standard SFA models and a misspecification problem.” The study uncovers a fundamental issue in Stochastic Frontier Analysis (SFA) models used to evaluate the performance of firms and industries.
October 24, 2025
Researchers from the Centre for Big Data in Economics and Finance have developed a new method for accurately identifying structural breaks in economic and financial time series. Their paper, "Change-Point Detection in Time Series Using Mixed Integer Programming," introduces a framework based on Mixed Integer Optimization (MIO).
October 22, 2025
The HSE Centre for Big Data in Economics and Finance is launching a regular iCEBDA Seminar Series. The new initiative serves as a natural continuation of the completed International Conference on Econometrics and Big Data Analysis (iCEBDA-25) and will be dedicated to modern methods in econometrics and data analysis.Центр больших данных в экономике и финансах НИУ ВШЭ запускает регулярную серию iCEBDA Seminar Series. Новая инициатива стала логичным продолжением прошедшей конференции International Conference on Econometrics and Big Data Analysis (iCEBDA-25) и будет посвящена современным методам эконометрики и анализа данных.С сентября по декабрь ведущие зарубежные и российские исследователи представят результаты своих работ в области панельных моделей, инструментально-свободных регрессий, анализа системных рисков и прогнозирования временных рядов.Ближайшие мероприятия29 сентября 2025 — Recent Development in Instrument-Free Approaches to Regression Models with Endogenous Regressors,
спикер: Kien C. Tran (University of Lethbridge).6 октября 2025 — Genuinely Robust Inference for Clustered Data,
спикер: Yulong Wang (Syracuse University).17 октября 2025 — Systemic Growth-at-Risk and Growth Spread Measures,
спикер: Abderrahim Taamouti (University of Liverpool).Полное расписание доступно на странице Центра.Организация и регистрацияОрганизатором серии выступает Центр больших данных в экономике и финансах НИУ ВШЭ.
Участие бесплатное, рекомендуется регистрация по ссылке.Семинары будут проходить в формате Zoom-конференций. Ссылка для подключения направляется зарегистрированным участникам и доступна на странице семинаров.
спикер: Kien C. Tran (University of Lethbridge).6 октября 2025 — Genuinely Robust Inference for Clustered Data,
спикер: Yulong Wang (Syracuse University).17 октября 2025 — Systemic Growth-at-Risk and Growth Spread Measures,
спикер: Abderrahim Taamouti (University of Liverpool).Полное расписание доступно на странице Центра.Организация и регистрацияОрганизатором серии выступает Центр больших данных в экономике и финансах НИУ ВШЭ.
Участие бесплатное, рекомендуется регистрация по ссылке.Семинары будут проходить в формате Zoom-конференций. Ссылка для подключения направляется зарегистрированным участникам и доступна на странице семинаров.
September 25, 2025