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).
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Paperback. Zustand: 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781108437493