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Illustrates Bayesian theory and application through a series of exercises in question and answer format.
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Joshua Chan is Professor of Economics at Purdue University, Indiana. He is interested in building flexible models for large datasets and developing efficient estimation methods. His favorite applications include trend inflation estimation and macroeconomic forecasting. He has co-authored the textbook Statistical Modeling and Computation (2013).
Gary Koop is a professor in the Department of Economics at the University of Strathclyde. He received his Ph.D. at the University of Toronto in 1989. His research work in Bayesian econometrics has resulted in numerous publications in top econometrics journals such as the Journal of Econometrics. He has also published several textbooks, including Bayesian Econometrics, and Bayesian Econometric Methods, and is co-editor of The Oxford Handbook of Bayesian Econometrics (2011). He is on the editorial board of several journals, including the Journal of Business and Economic Statistics and the Journal of Applied Econometrics.
Dale J. Poirier is Emeritus Professor of Economics and Statistics at the University of California, Irvine. He is a fellow of the Econometric Society, the American Statistical Association, the International Society for Bayesian Analysis, and the Journal of Econometrics. He has been on the Editorial Boards of the Journal of Econometrics and Econometric Theory, and was the founding editor of Econometric Reviews. His previous books include Intermediate Statistics and Econometrics: A Comparative Approach (1995), and The Econometrics of Structural Change (1976).
Justin L. Tobias is Professor and Head of the Economics Department at Purdue University, Indiana. He received his Ph.D. from the University of Chicago in 1999 and has contributed to and served as an Associate Editor for several leading econometrics journals, including the Journal of Applied Econometrics and the Journal of Business and Economic Statistics. His work focuses primarily on the development and application of Bayesian microeconometric methods.
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Bayesian Econometric Methods (Econometric Exercises, Series Number 7)
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Cambridge University Press, 2019
ISBN 10: 1108437494
ISBN 13: 9781108437493
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Paperback. Condizione: Like New. Second Edition. In nearly new condition: firm and square with strong joints, no creases. Just a few hardly noticeable rubs or very mild bumps. Hence a non-text page shows a small 'damaged' stamp. Despite such this book looks and feels unread. Thus the contents are crisp, fresh and tight. And so a very nice book in great condition, now offered for sale at a reasonable price. Codice articolo 203489
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Bayesian Econometric Methods (Econometric Exercises, Series Number 7)
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Bayesian Econometric Methods
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Bayesian Econometric Methods (Econometric Exercises, Series Number 7)
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Bayesian Econometric Methods (Econometric Exercises, Series Number 7)
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Bayesian Econometric Methods
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Cambridge University Press CUP, 2019
ISBN 10: 1108437494
ISBN 13: 9781108437493
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Bayesian Econometric Methods
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Cambridge University Press, 2019
ISBN 10: 1108437494
ISBN 13: 9781108437493
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Bayesian Econometric Methods (Econometric Exercises, Series Number 7)
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Bayesian Econometric Methods (Paperback)
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Cambridge University Press, Cambridge, 2019
ISBN 10: 1108437494
ISBN 13: 9781108437493
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Paperback. Condizione: new. Paperback. Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility models, ARCH, GARCH, and vector autoregressive models. The authors have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based and hierarchical specifications, models based upon latent variable representations, and mixture and time series specifications. MCMC methods are discussed and illustrated in detail - from introductory applications to those at the current research frontier - and MATLAB computer programs are provided on the website accompanying the text. Suitable for graduate study in economics, the text should also be of interest to students studying statistics, finance, marketing, and agricultural economics. The second edition of Bayesian Econometric Methods illustrates Bayesian theory and application through a series of exercises, complete with solutions to those exercises and computer code. The book is suitable for graduate students in statistics, economics, finance and other disciplines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781108437493
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Bayesian Econometric Methods (Econometric Exercises)
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Cambridge University Press 2019-08-15, 2019
ISBN 10: 1108437494
ISBN 13: 9781108437493
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Paperback. Condizione: New. Codice articolo 6666-GRD-9781108437493
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