Bayesian Econometric Methods

- Series Number 7: Bayesian Econometric Methods

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Bayesian Econometric Methods
Format:
Bog, hardback
Udgivelsesdato:
15-08-2019
Sprog:
Engelsk
Udgave:
2nd Revised edition.
  • Beskrivelse
  • Yderligere info
  • Anmeldelser

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 (R) 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.

Andre udgaver:

Bog, paperback

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Udgivelsesdato:
15-08-2019
ISBN13:
9781108423380
Vægt:
1120 g
Dybde:
26 mm
Bredde:
178 mm
Højde:
258 mm
Format:
Hardback
  • Forfattere

  • Bibliotekernes beskrivelse

    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. Worked examples or Exercises; 48 Tables, black and white; 50 Line drawings, black and white

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Bayesian Econometric Methods

- Series Number 7: Bayesian Econometric Methods

af

  • Leveringstid 4-6 hverdage
  • Forventet levering 27-09-2019

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