Bayesian Econometric Methods

Chan, Joshua and Koop, Gary and Poirier, Dale J. and Tobias, Justin L. (2019) Bayesian Econometric Methods. Cambridge University Press, Cambridge. ISBN 9781108423380

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Abstract

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.

ORCID iDs

Chan, Joshua, Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X, Poirier, Dale J. and Tobias, Justin L.;
CORE (COnnecting REpositories)