Bayesian Econometric Methods

Joshua Chan, Gary Koop, Dale J. Poirier, Justin L. Tobias

Research output: Book/ReportBook

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.
Original languageEnglish
Place of PublicationCambridge
PublisherCambridge University Press
Number of pages484
Edition2nd
ISBN (Print)9781108423380
Publication statusPublished - 15 Aug 2019

Keywords

  • Bayesian inference
  • finance
  • macroeconomics
  • Markov Chain Monte Carlo methods

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    Chan, J., Koop, G., Poirier, D. J., & Tobias, J. L. (2019). Bayesian Econometric Methods. (2nd ed.) Cambridge University Press.