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> Bayesian Econometric Methods

Bayesian Econometric Methods

Authors

Joshua Chan, Purdue University, Indiana, Gary Koop, University of Strathclyde, Dale J. Poirier, University of California, Irvine, Justin L. Tobias, Purdue University, Indiana
Published 2019

Description

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…

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Key features

  • Offers an update to the first edition by adding extensive coverage of macroeconomic models
  • Provides additional exercises to aid researchers new to MCMC with understanding the methods
  • MATLAB® computer programs are included on the website accompanying the text

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