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Econometrics and Statistics - Editorial Board

EcoSta Editors

Erricos J. Kontoghiorghes
Cyprus University of Technology and Queen Mary, University of London, UK, Cyprus
Editor-in-chief

Manfred Deistler
Vienna University of Technology, Austria
Co-editor Part A

Ana Colubi
University of Oviedo, Spain
Co-editor Part B


EcoSta Advisory Board - Part A (Econometrics)

Tim Bollerslev
Duke University, USA
Measuring, modeling, and forecasting financial market volatility

Francis X. Diebold
University of Pennsylvania, USA
Economic and financial measurement, modeling and forecasting, with emphasis on asset return volatility and correlation, yield curves, links to macroeconomic fundamentals, risk management, and business cycles

Robert Engle
New york University, USA
Macro economics, energy markets, urban economies and emerging markets, financial asset classes

Hashem Pesaran
University of Cambridge, USA
Heterogeneous panels with unobserved common effects, panel unit root tests, PVAR, long-run structural macroeconometric modelling, GVAR, structural breaks, financial econometric s

Peter C.B. Phillips
Yale University, University of Auckland, Singapore Management University, University of Southampton., USA
Time series, panels, trends, bubbles, financial warning alert systems

Mike West
Duke University, USA
Bayesian statistics involving stochastic modelling in higher-dimensional problems: dynamic models in time series analysis, multivariate analysis, latent structure, stochastic computational methods, parallel/GPU computing


EcoSta Advisory Board - Part B (Statistics)

Peter Buehlmann
ETH Zurich, Switzerland
Statistics, machine learning, computational biology

Peter Green
University of Bristolvand University of Technology, Sydney, UK and Australia
Bayesian inference in complex stochastic systems, Markov chain Monte Carlo methodology, forensic genetics, Bayesian nonparametrics graphical models

Xuming He
University of Michigan, USA
Robust statistics, quantile regression, subgroup analysis, model selection

Steve Marron
University of North Carolina at Chapel Hill, USA
Object oriented data analysis, smoothing methods for curve estimation

Hans-Georg Mueller
University of California Davis, USA
Functional data, longitudinal data

Byeong Park
Seoul National University, Korea, South
Nonparametric inference, functional data analysis

Ingrid Van Keilegom
Universite catholique de Louvain, Belgium
Cure model, survival analysis, measurement error, semiparametric regression, SIMEX


EcoSta Associate Editors - Part A (Econometrics)

Sung Ahn
Washington State university, United States
Multivariate Time Series, Cointegration

Alessandra Amendola
University of Salerno, Italy
Time series, nonlinear models, forecasting, financial data analysis

Monica Billio
University Ca' Foscari of Venice, Italy
Dynamic latent factor models, simulation-based Inference, volatility and risk modelling, switching regime models, volatility transmission and contagion, business cycle analysis, hedge funds, systemic risk

Jean-Marie Dufour
McGill University, Canada
Econometrics, time series, structural models, identification, macroeconomics, financial econometrics

Andrew Harvey
University of Cambridge, UK
Time series and econometrics, macroecometrics and financial econometrics, state space models, signal extraction, volatility, quantiles and copulas.

Alain Hecq
Maastricht University, Netherlands
Co-movements, business cycles, mixed frequency, cointegration, common cycles, VAR, noncausality

Masayuki Hirukawa
Ryukoku University, Japan
Nonparametric estimation and inference, Asymmetric kernels, Econometrics of data combination, Applied econometrics

Degui Li
University of York, UK
Time series, nonparametric and semiparametric statistics, panel data

Gael Martin
Monash University, Australia
Bayesian econometrics, simulation methods, non-Gaussian time series analysis

Yasuhiro Omori
University of Tokyo, Japan
Bayesian analysis, Bayesian econometrics, Markov chain Monte Carlo, stochastic volatility, state space model

D.S.G. Pollock
University of Leicester, UK
Statistical analysis in the frequency domain, filtering methods, wavelets, econometric methods, time series analysis, functional analysis

Tommaso Proietti
Università di Roma Tor Vergata, Italy
Time series analysis, applied econometrics, unobserved components models, forecasting methods

Artem Prokhorov
University of Sydney, Australia
Econometric theory, semiparametric estimation and inference in econometrics, copulas and dependence structures and measures, heavy tailed distributions and robust inference, stochastic frontier analysis, financial applications

Zacharias Psaradakis
Birkbeck University of London, UK
Time-series econometrics, bootstrap methods, nonlinear models, applied econometrics

Jeroen V.K. Rombouts
ESSEC Business School, France
Financial econometrics, volatility, option pricing, times series forecasting, Bayesian times series

Willi Semmler
New School for Social Research, USA
Empirical macroeconomics, business cycles, macro dynamics, dynamic portfolio modeling, multi regime models, multi regime VAR, dynamic programming, Nonlinear Model Predictive Control

Mike K.P. So
Hong Kong University of Science and Technology, China
Bayesian analysis, financial time series modeling, market volatility study, risk management

Mark Steel
University of Warwick, UK
Bayesian inference, models with unobserved heterogeneity, MCMC methods, inference robustness, model choice and Bayesian model averaging, improper and reference priors, mixture modelling, skewness, inference in stochastic processes, spatial statistics, semi- and nonparametric Bayesian, growth theory, stochastic frontier models, contingent valuation, stochastic volatility models

Robert Taylor
University of Essex, UK
Bootstrap methods for non-stationary time series, co-integration methods, (seasonal) unit root tests, stationarity tests, stochastic volatility, persistence change testing and structural breaks, financial econometrics

Carsten Trenkler
Universitaet Mannheim, Germany
Time series analysis, cointegration, bootstrap

Alan Wan
City University of Hong Kong, Hong Kong
Model averaging and model selection, Varying-coefficient models, Quantile regression, Censored, length-biased and missing data

Peter Winker
University of Giessen, Germany
Time series modeling, forecasting, model selection, optimization heuristics in statistics and econometrics


EcoSta Associate Editors - Part B (Statistics)

Eric Beutner
University of Maastrich, Netherlands
Weighted empirical processes, (quasi)-Hadamard differentiability, weakly dependent data, strongly dependent data, statistical functional like conditional tail expectation and U-statistics, order statistics, progressively Type-II censored random variables, sequential order statistics, non-parametric methods in reliability/survival analysis, semi-parametric methods in reliability/survival analysis

Ming-Yen Cheng
National Taiwan University, Taiwan
High-dimensional data, non- and semi-parametric models

Bertrand Clarke
University of Nebraska-Lincoln, USA
Data mining and machine learning, prediction,statistical techniques for complex or high-dimensional data, model bias and uncertainty

Aurore Delaigle
University of Melbourne, Australia
Nonparametric estimation, measurement errors, deconvolution problems, functional data analysis

John Einmahl
Tilburg University, Netherlands
Statistics of extremes, empirical processes, multivariate quantiles, empirical likelihood

Frederic Ferraty
University of Toulouse, France
Functional data analysis, high dimensional data, non/semi-parametric modelling, model selection, theory and practice

Roland Fried
TU Dortmund University, Germany
Time series, changepoints, robustness, outliers

Armelle Guillou
Strasbourg, France
Computer-intensive statistical methodologies such as bootstrap, jackknife and other resampling methods, extreme value inferences and their applications, statistical inferences in presence of censoring and/or truncation, robust and nonparametric methods

Michele Guindani
UCLA, USA
Bayesian Analysis, Bayesian Nonparametrics, Biostatistics, Statistical decision making, multiple hypotheses testing

Marc Hallin
Universite Libre de Bruxelles, Belgium
Time series, factor models, asymptotic theory of statistical experiments

Raphael Huser
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Statistics of extremes, Spatio-temporal statistics, Copulas, Computational statistics, Environmental and financial applications

Ivan Kojadinovic
University of Pau, France
Multivariate analysis, nonparametric statistics, copulas, change-point detection, empirical processes, environmental and financial applications.

Piotr Kokoszka
Colorado State University, USA
Functional data analysis, time series and spatial statistics, applications to financial econometrics

Yoonkyung Lee
The Ohio State University, USA
Statistical learning, multivariate analysis, kernel methods, classification

Christophe Ley
Universite Libre de Bruxelles, Belgium
Optimal inferential procedures, rank-based procedures, non-Gaussian distributions, directional data, Maximum Likelihood Estimation, Non- and semi-parametric statistics, High-dimensional inferential procedures

Lola Martinez-Miranda
University of Granada, Spain
Nonparametric estimation, kernel smoothing,non-life insurance,bootstrap, bandwidth

Domingo Morales
University Miguel Hernandez of Elche, Spain
Small area estimation, statistical information theory, simulation and resampling methods, survey sampling, asymptotic statistics, statistical models

Igor Pruenster
University of Torino, Italy
Bayesian asymptotics, Bayesian inference, Bayesian survival analysis, distribution theory, mixture models, predictive inference, random measures, species sampling

Wolfgang Trutschnig
University of Salzburg, Austria
Dependence Modeling and Copulas, Nonparametric Methods

Stefan Van Aelst
Ghent University, Belgium
Robustness, multivariate analysis, model selection

Germain Van Bever
Universite Libre de Bruxelles, Belgium
Functional data analysis, depth, nonparametric statistics, supervised classification, classification, high-dimensional statistics, information geometry, computational geometry.

Mattias Villani
Linkoping University, Sweden
Bayesian inference, machine learning, computational statistics, predictive inference

Alastair Young
Imperial College London, United Kingdom
Statistical theory, computational statistics, statistical asymptotics and approximation methods, bootstrap, likelihood-based inference

Ding-Xuan Zhou
City University of Hong Kong, Hong Kong
Learning theory, deep neural networks, approximation theory, wavelets and applied harmonic analysis


For further information please send an email to editorial.office@econometricsandstatistics.org