The 6th International Conference on Econometrics and Statistics (EcoSta 2023) will be hosted by Waseda University, Tokyo, Japan, 1-3 August 2023. A tutorial will be given on Monday, the 31st of July 2023.
The conference will be held in a hybrid form. The keynote talks, the special invited sessions, the hybrid organized sessions, and the virtual sessions will be live-streamed for all the conference participants. Contributed speakers can choose in-person or virtual presentation, while invited speakers should coordinate their presentation mode with the session organizers. All the posters will be posted online, but in-person participants will be able to meet physically during the poster session.
The 5th International Conference on Econometrics and Statistics, EcoSta 2022, has taken place at the Ryukoku University, Kyoto, Japan, and gathered 850 in-person and virtual participants.
This hybrid conference is co-organized by the Working Group on Computational and Methodological Statistics (CMStatistics), the network of Computational and Financial Econometrics (CFENetwork), and Waseda University.
The journals Econometrics and Statistics (EcoSta) and Computational Statistics & Data Analysis (CSDA) and their special sections, the Annals of Computational and Financial Econometrics, and Annals of Statistical Data Science are the main sponsors. Selected peer-reviewed papers will be considered for publication in special or regular issues of the journals Econometrics and Statistics, and Computational Statistics & Data Analysis.
For further information, please contact firstname.lastname@example.org or info@CFEnetwork.org.
This conference invites oral and poster presentations containing substantial advances in the broad areas of econometrics and statistics. All topics within the scope of the journal Econometrics and Statistics will be considered. Topics of interest include, but are not limited to:
Part A. Econometrics: estimation of econometric models and associated inference, model selection, panel data, measurement error, time series analyses, filtering, portfolio allocation, option pricing, quantitative risk management, systemic risk and market microstructure, forecasting, volatility and risk, credit risk, pricing models, portfolio management and emerging markets.
Part B. Statistics: high-dimensional problems, functional data analysis, robust statistics, resampling, dependence, extreme value theory, spatial statistics, Bayesian methods, statistical learning, nonparametric statistics, multivariate data analysis, parametric & semiparametric models, numerical methods in statistics, and substantial statistical applications in other areas such as medicine, epidemiology, biology, psychology, climatology and communication. Innovative algorithmic developments are welcome, as are the computer programs and the computational environments that implement them as a complement.