The COVID-19 pandemic does not allow to safely hold large in-person events currently. Thus, the 4th International Conference on Econometrics and Statistics (EcoSta 2021) will take place virtually. The virtual conference will be hosted by the Hong Kong University of Science and Technology, Hong Kong, 24-26 June 2021. Tutorials will be given on Wednesday 23rd of June 2021. All the scientific sessions will take place virtually. Still, if the health conditions and the local regulations allow in-person meetings by the conference time, the organizers will consider holding in-person networking events in Hong Kong.
The 3rd International Conference on Econometrics and Statistics, EcoSta 2019, has taken place at the National Chung Hsing University (NCHU), Taiwan, 25-27 June 2019, and gathered about 660 participants. In 2020, EcoSta was postponed due to the COVID-19 pandemic.
This virtual conference is co-organized by the Working Group on Computational and Methodological Statistics (CMStatistics), the network of Computational and Financial Econometrics (CFENetwork), and The Hong Kong University of Science and Technology (HKUST) Business School.
The journal Econometrics and Statistics (EcoSta) and its supplement, the Annals of Computational and Financial Econometrics, and the Computational Statistics & Data Analysis are the main sponsors of the conference. Selected peer-review papers will be considered for publication in a special peer-reviewed, or regular, issues of the Journals Econometrics and Statistics, and Computational Statistics & Data Analysis.
This conference invites oral and poster presentations containing substantial advances in the broad areas of econometrics and/or statistics. All topics within the scope of the journal Econometrics and Statistics will be considered. Topics of interest include, but 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.