The 3rd International Conference on Econometrics and Statistics (EcoSta 2019) will take place at the National Chung Hsing University (NCHU), Taichung, Taiwan 25-27 June 2019.

The 2nd International Conference on Econometrics and Statistics, EcoSta 2018 has taken place at the City University of Hong Kong, Hong Kong 19-21 June 2018, and gathered about 620 participants.

This Conference is co-organized by the Working Group on Computational and Methodological Statistics (CMStatistics), the network of Computational and Financial Econometrics (CFENetwork), the Department of Applied Mathematics and the Institute of Statistics of the National Chung Hsing University.

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.

For further information please contact info@cmstatistics.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 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 & semi parametric 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.