جست‌وجوی استاد و پژوهشگر

Peter Michael Robinsonمشاهده پروفایل

استاد

• Econometrics
• time series analysis
• spatial analysis
• +۲ مورد دیگر

Peter Michael Robinson holds the Tooke Professorship of Economic Science and Statistics at the London School of Economics and Political Science (LSE), where he maintains an active research profile in advanced econometric theory. His institutional affiliation centers on LSE's Department of Economics, though the departmental structure is not explicitly detailed in available materials. Robinson's research program focuses on cutting-edge econometric methodologies, particularly long-memory processes, spatial dependence structures, and nonstationary time series analysis. His work bridges theoretical rigor with practical applications in panel data and spatial econometrics, emphasizing semiparametric and nonparametric approaches for complex dependency patterns. Key innovations include refined inference techniques for spatial autocorrelation and fractional integration models. Analysis of his recent publications (2012-2018) reveals a sustained emphasis on spatial econometrics and time-series methodology, with significant contributions to panel data modeling, long-range dependence theory, and nonparametric estimation under cross-sectional dependence. His work consistently advances asymptotic theory while addressing real-world data challenges in economics and statistics. No scientific awards or honors were explicitly documented in the source materials. Available records contain no information regarding doctoral students, grant funding, or academic advising activities. Research infrastructure details such as laboratories or collaborative teams were not specified in the provided documentation.

Nan Zouمشاهده پروفایل

مدرس

• Time series analysis
• Extreme value theory
• Resampling-type methods
• +۳ مورد دیگر

Nan Zou is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. She holds a Ph.D. in Mathematics with a Specialization in Statistics from the University of California San Diego (2017) and a B.B.A. in Management Science from Renmin University of China (2011). Her research focuses on statistical methodology and theory in time series analysis, extreme value theory, resampling techniques, and dynamical systems. She has collaborated internationally and received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her recent work addresses bias correction in extreme value analysis, bootstrap methods for unit root testing, and multivariate time series extremes. Key contributions include reweighted madogram estimators and seasonal unit root tests under periodic variation. Current research emphasizes statistical inference for massive datasets and dynamical systems. Nan Zou actively supervises PhD and MRes students at Macquarie University, encouraging applications from eligible candidates. She has published in journals like the Annals of Statistics and Journal of Nonparametric Statistics , with a focus on methodological advancements in statistical theory.

• Econometrics
• Time Series Analysis
• Copula Models
• +۹ مورد دیگر

Brendan Kinnane Beare is a Professor of Econometrics at the University of Sydney , affiliated with the School of Economics under the Faculty of Arts and Social Sciences. His research focuses on econometric theory, time series analysis, copula modeling, and stochastic processes. Education: PhD in Economics (2007) from Yale University Research areas: Time series, copulas, Markov modulation, unit root testing, financial econometrics Recent work includes stochastic arbitrage with options, Pareto exponents in economic models, and tests for copula symmetry Beare's publication trends span econometric theory, applied statistics, and financial modeling, with a focus on mathematical rigor and empirical relevance. He teaches undergraduate and postgraduate econometrics courses and participates in international seminars on unit roots and cointegration.

• Statistics
• Deep Learning
• Probability
• +۷ مورد دیگر

Hailin Sang is an Associate Professor in the Department of Mathematics at the University of Mississippi , affiliated with the College of Liberal Arts . He holds a Ph.D. in Mathematics from the University of Connecticut (2008) and has previously held visiting assistant professor and postdoctoral research fellow positions at institutions including the University of Cincinnati, National Institute of Statistical Sciences/Duke University, and Indiana University. Dr. Sang’s research spans theoretical and applied statistics, with emphasis on deep learning , probability theory , empirical processes , time series analysis , random fields , nonparametric and robust statistics , and self-normalized statistics . His work also extends to survey sampling design and analysis . Recent publications highlight applications in generative adversarial networks , modified ReLU networks , and entropy estimation , with methodological contributions to limit theorems and deviation bounds for complex stochastic structures. His research has received partial support from the Simons Foundation . He has not been explicitly recognized for scientific awards in the provided text. Dr. Sang teaches a range of courses from elementary statistics to advanced statistics seminars , reflecting his broad educational contributions.

Tomás Del Barrio Castroمشاهده پروفایل

استاد

• Time Series Analysis
• Seasonality
• Applied Econometrics
• +۲ مورد دیگر

Dr. Tomás Del Barrio Castro is a Full Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB) , where he has been affiliated since 2007. He holds a PhD in Economics (University of Barcelona, 1998) and a Degree in Economics (Autonomous University of Madrid, 1992). PhD in Economics, University of Barcelona (1998) Degree in Economic and Business Sciences, Autonomous University of Madrid (1992) His research focuses on Time Series Analysis , Seasonality , and Applied Econometrics , with methodological contributions to seasonal unit root testing and periodic integration. He has published in top journals including Econometric Theory , The Econometric Journal , and Journal of Time Series Analysis . A significant portion of his work examines temporal aggregation effects, persistent cycles, and mixed-frequency data challenges. Scientific Recognition : Econometric Theory Multa Scripsit Award (2014) As Principal Investigator since 2007, he leads competitive research projects in econometrics. His teaching spans undergraduate and graduate courses in Statistics and Econometrics, including Macroeconometrics and Time Series Analysis at UIB and University of Barcelona. Recent appointments include: 2025-26: Macroeconometrics (Double Degree in Economics & Tourism) 2025-26: Time Series Analysis (Master's in Big Data Analysis)

• Econometrics
• Time Series Analysis
• Macroeconomic Forecasting
• +۴ مورد دیگر

Mark W. Watson is the Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs at the Princeton University Department of Economics . His research focuses on macroeconomic time series analysis , with particular emphasis on dynamic factor models , inflation forecasting , and structural vector autoregression methods. Research Interests : Watson's work addresses fundamental econometric challenges in Time series modeling under structural instability Robust inference for nonstationary data Macroeconomic forecasting techniques Analysis of business cycle dynamics Cointegration and unit root testing Monetary policy and inflation relationships Publication Trends show expertise in high-dimensional forecasting , factor models , and robust statistical methods applied to macroeconomic data. His collaborative work with James H. Stock has become foundational in modern econometric practice. Technical Contributions include development of New index construction for economic indicators Software tools for cointegration analysis Replication frameworks for empirical research Available through Princeton University servers.

• Applied Economics
• Econometrics
• Statistics
• +۳ مورد دیگر

Prof Svetlana Makarova is a Professor of Applied Economics at the School of Slavonic and East European Studies (SSEES), University College London. She holds a PhD from the Russian Academy of Sciences (1985) and a Diploma from St. Petersburg State University (1978). Her research focuses on applied economics, econometrics, statistics, and economic theory, with a strong emphasis on inflation dynamics, uncertainty modeling, monetary policy, and macroeconomic forecasting. She is particularly known for her work on inflation forecast uncertainty using advanced statistical methods like copula approaches and skew normal distributions, as well as analyses of economic policies in transitional and emerging economies. Prof Makarova's expertise includes the application of econometric models to understand macroeconomic phenomena such as deflation episodes, high inflation impacts, and central bank policy effects. Her work frequently addresses Eastern European and post-Soviet economies, leveraging interdisciplinary methods like natural language processing for textual analysis. She is fluent in English, Russian, and Polish, enhancing her ability to conduct cross-cultural economic research. Her contributions span both theoretical advancements in econometric modeling and practical policy-oriented analyses. Her research has explored topics ranging from stochastic unit-root bilinear processes to inter-country economic models linking Belarus, Russia, and Ukraine. She has also investigated the real effects of monetary policy in Poland and Russia, emphasizing timing and supply-side impacts. Despite her prolific publication record, no specific scientific awards are mentioned, though her extensive academic positions and contributions reflect her scholarly impact.

Francisco Blasquesمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Data Science
• +۵ مورد دیگر

Francisco Blasques is a Full Professor of Econometrics and Data Science at the Vrije Universiteit Amsterdam (VU) and a Research Fellow at the Tinbergen Institute. He holds leadership roles, including Co-founder of Churned (QuantIQ) and datastuff BV, and served as TI Doctoral Director for the School of Business and Economics. His research focuses on econometrics, statistics, and data science, with expertise in time-series analysis, financial econometrics, and nonlinear dynamics. Teaching includes advanced econometrics courses at VU and Tinbergen Institute, covering topics like time-series modeling and financial econometrics. His PhD supervision spans over 15 students since 2012, focusing on econometric methods and applications. Research contributions emphasize score-driven models, volatility analysis, and spatial econometrics. Key publications address topics such as natural disaster impacts on economies, robust volatility estimation, and fusion of experimental/observational data. Awards include the NWO-VIDI Grant (2019) for methodological innovations in econometrics. Professional activities include ancillary roles as Partner at QuantiQ BV and Director at Metyis, alongside academic duties. His work contributes to UN Sustainable Development Goals through economic resilience and policy analysis.

• Economics
• Environmental Economics
• Energy Policy
• +۳ مورد دیگر

Svetlana Maslyuk is a Lecturer and Researcher affiliated with the School of Arts and Humanities within the Faculty of Education and Arts. Her research spans economics, environmental policy, and financial systems, with a focus on energy consumption patterns, economic growth determinants, and tax revenue dynamics in developing nations. She has collaborated with institutions across multiple countries, contributing to interdisciplinary studies in applied economics and education. Her research interests prominently feature the intersection of energy policy and economic development, particularly in Africa and Asia. She also explores educational assessment practices in higher education, as evidenced by collaborative work on student perspectives in university settings. Key research trends in her publications include analyzing stochastic convergence in energy consumption, evaluating stock market impacts on economic growth, and investigating the environmental and fiscal implications of carbon taxation. Her work frequently employs advanced econometric techniques such as panel data analysis and cointegration testing. Maslyuk has authored/co-authored over a dozen peer-reviewed journal articles and book chapters, with contributions to outlets like Applied Economics , Journal of Developing Areas , and Energy Economics . Her research has addressed diverse topics including renewable energy adoption, Malaysian tax-revenue linkages, and Australian coal mining resilience to economic shocks.

Uwe Hasslerمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Statistical Methods
• +۳ مورد دیگر

Prof. Dr. Uwe Hassler is a Professor of Statistics and Econometric Methods at Goethe University Frankfurt, affiliated with the Department of Economic Policy and Quantitative Methods within the Faculty of Economics and Business Economics. His research focuses on time series analysis, econometric methodology, long memory processes, and unit root testing. He holds an office at RuW 3.214 on Theodor-W.-Adorno-Platz 4 in Frankfurt. Key research interests include statistical inference, hypothesis testing pitfalls, and applications in financial econometrics. Recent work addresses self-normalizing tests, spurious correlations in random walks, and historical mathematical problems like the Basel conjecture. His contributions span theoretical advancements and practical methodologies in time series analysis. Publications emphasize rigorous testing frameworks, addressing issues like sample size determination, significance testing pitfalls, and long memory properties in economic data. Notable collaborations include work on inflation dynamics and cointegration analysis. No academic awards or grants are explicitly listed in provided texts. His team includes researchers like Tanja Zahn and lecturers such as Balázs Cserna, contributing to the Hassler research group.

Jaya Bishwalمشاهده پروفایل

دانشیار

• Probability
• Stochastic Processes
• Statistical Inference
• +۲ مورد دیگر

Dr. Jaya Bishwal is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research expertise centers on Probability and Stochastic Processes, with applications in mathematical finance and statistical inference. Her extensive publication record demonstrates deep specialization in fractional processes, stochastic differential equations, and statistical estimation methods. Recent work focuses on advanced econometric applications including Levy process modeling, fractional Ornstein-Uhlenbeck systems, and volatility estimation in financial mathematics. Methodological innovations include developments in quasi-likelihood estimation, Kolmogorov distance analysis for estimators, bootstrap methods for financial derivatives, and asymptotic theory for nonergodic systems. Her research bridges theoretical probability and practical applications in quantitative finance.

• Econometrics
• Time Series Analysis
• Bootstrap Inference
• +۳ مورد دیگر

Iliyan Georgiev is an Associate Professor of Econometrics at the Faculty of Economics, University of Bologna, since 2018. Previously, he held roles at Nova School of Business and Economics (2011–2015) and the Agency for Economic Analysis and Forecasting in Sofia (1995–1998). He obtained a PhD in Economics from the European University Institute (2004), and degrees in Mathematics and International Economic Relations from Sofia University and Karl Marx Institute of Economics. His research focuses on econometric theory, time series analysis, bootstrap inference, and spurious correlations. He teaches Econometrics and Mathematics, including differential calculus and stochastic calculus. Georgiev’s work emphasizes robust statistical methods for non-stationary processes and rare events, with applications in financial and economic forecasting. His recent publications explore predictive regression, canonical correlation analysis, and unit root testing under heavy-tailed distributions. He has contributed to methodologies addressing parameter instability and spurious correlations in time series data. No explicit scientific awards are listed, but he achieved National Scientific Qualification for Full Professor in Econometrics (MIUR, 2018). No grants or lab affiliations are mentioned in the provided texts.

• Time Series Analysis
• Statistical Modeling
• Climate Science
• +۴ مورد دیگر

Hans Arnfinn Karlsen is an Associate Professor at the Department of Mathematics, University of Bergen. His research focuses on statistical modeling, time series analysis, and their applications in climate science, geophysics, and petroleum engineering. He has contributed to spatio-temporal GARCH models, volatility analysis, and data assimilation techniques. Affiliations: University of Bergen (Department of Mathematics), NORCE Norwegian Research Centre. Research interests include non-linear time series, cointegration, GARCH models, and statistical methods for environmental data. Key work includes analyzing temperature variability on Svalbard, oil well data segmentation, and adaptive Gaussian mixture filters. His recent articles explore climate variability trends, spatio-temporal volatility modeling, and large sample properties of filtering algorithms. Collaborations involve researchers in statistics, petroleum engineering, and environmental science.

Rolf Scheufeleمشاهده پروفایل

پژوهشگر

• applied econometrics
• macroeconomic modelling
• DSGE models
• +۲ مورد دیگر

Rolf Scheufele is a Research Affiliate at the Halle Institute for Economic Research (IWH) since March 2012 and an Economist at the Swiss National Bank . He specializes in applied econometrics and macroeconomic modelling , with a focus on GDP forecasting, dynamic stochastic general equilibrium (DSGE) models, and economic policy analysis. Institution: Halle Institute for Economic Research (IWH) – Member of the Leibniz Association Department: Macroeconomics Current Role: Economist at Swiss National Bank His research explores the use of real-time data versus final data in GDP prediction, disaggregation techniques for macroeconomic forecasting, and the reliability of impulse response analysis in misspecified DSGE models. He has contributed significantly to understanding convergence processes in East Germany and evaluating the New Keynesian Phillips Curve for Germany. Key trends in his publications include: Integration of mixed-frequency models and real-time data for accurate GDP forecasting Comparison of full information and limited information estimation techniques in macroeconomic modeling Analysis of price rigidity , economic shocks , and regional convergence in Germany His work is conducted in collaboration with institutions like the Halle Institute for Economic Research (IWH) , where he contributes to macroeconomic research and policy evaluation.

Karsten Webelمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Seasonal Adjustment
• Stochastic Processes
• +۳ مورد دیگر

Karsten Webel is a Research Professor at the Deutsche Bundesbank's Research Centre, part of the Directorate General Data and Statistics. His work focuses on seasonal adjustment methodologies, stochastic processes, machine learning applications, and index theory. He contributes to the development and implementation of JDemetra+, the official software for seasonal adjustment in Europe. Webel's research emphasizes infra-monthly time series analysis, including daily and weekly economic indicators, and has published extensively on topics like random forest-based seasonality tests and data-driven model selection. Education and affiliations are not explicitly detailed in the provided text, but Webel's professional activities include presentations at conferences such as the International Conference on Establishment Statistics and the Joint Statistical Meetings. His research has been disseminated through peer-reviewed journals, conference proceedings, and book chapters, with a strong emphasis on methodological advancements in time series analysis and econometrics. Key research interests include seasonal adjustment techniques for non-traditional time frequencies, stochastic processes, and the application of machine learning algorithms to economic data. Webel collaborates with institutions like Insee and the National Bank of Belgium, contributing to the Eurostat-recommended seasonal adjustment software framework. His work bridges theoretical statistical methods with practical applications in central banking and official statistics.