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Contemporary Bayesian Econometrics And Statistics Geweke

John Geweke, Distinguished Professor, Economics Discipline Group, University of Technology Sydney,Gary Koop, Professor of Economics, University of Strathclyde,Herman van Dijk, Professor of Econometrics, Econometric Institute, Erasmus University Rotterdam and Econometrics Department, VU University AmsterdamJohn Geweke received his PhD in economics from the University of Minnesota. He has been Professor of economics/ statistics at the University of Wisconsin, Duke University, the University of Minnesota, and the University of Iowa. He is co-editor of Journal of Econometrics, past co-editor of Journal of Applied Econometrics, and past editor of Journal of Business and Economic Statistics. He has published widely in econometrics and statistics, with major contributions to the analysis of time series and Bayesian econometrics. Professor Geweke is an elected fellow of the Econometric Society and the American Statistical Association and a past President of the International Society for Bayesian Analysis. Gary Koop has published numerous articles in Bayesian econometrics and statistics in journals such as Journal of Econometrics, Journal of the American Statistical Association and the Journal of Business and Economic Statistics. He is an associate editor for several journals, including Journal of Econometrics and Journal of Applied Econometrics. He is the author of Bayesian Econometrics, Bayesian Econometric Methods, Introduction to Econometrics, Analysis of Economic Data, and Analysis of Financial Data.Herman van Dijk received the Savage Prize for his PhD dissertation. His research interests are in Bayesian inference using simulation techniques, time series econometrics, and income distributions. He serves on the Editorial Board of major journals in econometrics. His publications consist of several books and more than 160 international scientific journal papers and reports.

contemporary bayesian econometrics and statistics geweke

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Although the use of the Bayesian approach to statistical inference in applied research has been increasing over the last few decades, the classical/frequentist approach still dominates the landscape, at least in the field of econometrics. Most published scientific articles which involve some form of data analysis are based on classical methods. Similarly, textbooks on statistical inference are overwhelmingly frequentist and, usually, cover Bayesian methods only within a single chapter or section. Nevertheless, there are quite a few general statistics textbooks available, which concentrate on the Bayesian approach and a few specifically covering Bayesian econometrics.

This section of the website provides references to some of the textbooks which cover Bayesian econometrics, either extensively or exclusively. It also has a brief overview of Bayesian statistical software packages which are popular among econometricians. The emphasis is on econometrics and not general statistics, because the notation and terminology used therein should be familiar to the users of BayES.

The summer school offers a selective introduction to program evaluation methods in econometrics. The focus will be mostly on methodological developments, but applications will also be discussed as necessary. It would be ideal if participants had elementary working knowledge of statistics and econometrics at the master level, but the lectures will be self-contained. Topics covered include an introduction to Bayesian methods, Markov-switching VAR and Markov-switching DSGE, Textual Analysis, forecasting with machine learning techniques, Structural time series representations, Recent advances in the estimation of dynamic causal effects, Identification and inference for impulse responses, Regressions in impulse response space, Policy counterfactuals and Optimal policy perturbations with impulse responses.


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