Center for econometrics
and business analytics





Projects in progress

Read the latest news from the Center about |

About us
and our mission

Interdisciplinary Research Center for Econometrics and Business Analytics (CEBA) has been in creation at St.Petersburg State University since 2016 as part of activities funded by the Russian Science Foundation (RSF) priority area grants.

In early 2021, the Center became part of SPSU and received funding from the University Endowment Fund.

CEBA is an international group of researchers conducting cutting-edge research in the intersection of economics, finance, statistics and machine learning.

Laboratory mission - to obtain scientific results of international caliber.
To create and foster the country's first world-class research lab in the field of econometrics and business analytics.
To attract students and early career researchers from the region's universities to high-level international research projects in economics, finance, econometrics and business-analytics.
To facilitate quantitative and interdisciplinary approaches to teaching economics and management disciplines in Russia.

Center goals

Spheres of work
Now the laboratory is working in these areas. If you have not found an area of interest to you, you can always suggest it to the center.
Time Series Econometrics
We study massive datasets such as stock returns. We develop and apply models of financial bubbles, regime shifts. We design flexible and robust estimation techniques, applicable in the presence of heavy tails and unknown dependence patterns.
Productivity and Efficiency Analytics
We study ways to best assess firm's productivity and to make firms more efficient. We estimate production functions and study how production units rank in their use of inputs.
Network Analytics
We develop new methods for the analysis of social, natural and man-made networks. We study their statistical properties and use network data to improve network performance.
We study price formation on art markets. We develop new methods for machine learning in art. We model behavior of museum goers.
Art Analytics
RSF Grant № 19-18-13029
Modern methods of robust inference in finance and economics, with applications to the study of crises and their propagation in financial and economic markets.
RSF Grant № 20-18-00365
Robust methods and models for resilient markets and efficient production lines
RSF Grant № 20-78-10113
New methods of robust inference for developing markets: Financial bubbles, time-varying volatility, structural breaks and beyond.
RFBR Grant № 20-010-00960
New sustainable methods for analyzing emerging securities markets: Market efficiency, clusters of volatility, nonlinear dependence and predictability.