EcoSta 2017

The 1st International Conference on Econometrics and Statistics (EcoSta 2017) will take place at the Hong Kong University of Science and Technology, Hong Kong 15-17 June 2017.

This Conference is co-organized by the Working Group on Computational and Methodological Statistics (CMStatistics), the network of Computational and Financial Econometrics (CFENetwork) and 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.

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Aims and Scope

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, high-dimensional problems, functional data analysis, robust statistics, resampling, dependence, extreme value theory, spatial statistics, Bayesian methods, nonparametric statistics, multivariate data analysis, parametric & semi parametric models, numerical methods in statistics and econometrics, the 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, emerging markets, 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.