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Peter M. Robinson


Peter M. Robinson - The London School of Economics and Political Science (UK)

Peter M. Robinson received his PhD from the Australian National University, and holds an MSc, with distinction, from the London School of Economics. In 2000 he was awarded an Honorary Doctorate by Carlos III University of Madrid. Dr. Robinson is Tooke Professor of Economics and Statistics at the London School of Economics. He has also been Associate Professor at Harvard University and the University of British Columbia, professor at the University of Surrey, and Visiting Professor at UC Berkeley, Yale, and the Institute for Advanced Studies in Vienna. He is a Fellow of the British Academy, the Econometric Society, Institute of Mathematical Statistics, Institute of Mathematical Statistics and Centre for Microdata Methods and Practice and a member of the International Statistical Institute and the Royal Society for the encouragement of Arts, Manufactures and Commerce. Peter M. Robinson is Editor of the Journal of Econometrics and previously of the Econometric Theory and Econometrica. He’s currently Associate Editor of the Journal of Time Series Analysis, Annals of Statistics y Statistical Inference for Stochastic Processes. He’s been Associate Editor of The Review of Economics and Statistics, The American Statistician, Infor, Journal of Econometrics, Econometric Theory, Econometrica, Journal of the Royal Statistical Society, Series A, International Statistical Review, Econometric Reviews, Journal of Nonparametric Statistics, Statistica Sinica and Journal of the American Statistical Association.


Project: The project will develop methods and theory relating to cross-sectional dependence in econometric data, specifically data that are observed as a cross-section or panel, with particular focus on situations where the number of cross-sectional units is large. The following specific topics will be considered: inference in the presence of strong cross-sectional dependence; hypothesis testing for cross-sectional dependence; cross-sectional dependence in mixed-rate problems; inference with regularized cross-sectional data; nonparametric trends with cross-sectional dependence; stochastic trends with cross-sectional dependence. In each case, new methods of estimation and/or hypothesis testing will be developed, and rigorous theoretical justification established, in the form of large-sample statistical theory. In addition, finite-sample properties will be examined by means of Monte Carlo simulations.

Stay Period: JAN 13 - MAR 13