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Regular version of the site

Centre for Big Data in Economics and Finance

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

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
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
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.
September 25
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