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  3. Bayesian Econometrics

Bayesian Econometrics (ECOM90010)

Graduate courseworkPoints: 12.5Not available in 2019

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Overview

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The overall aim of this subject is to introduce students to the essential concepts and techniques/tools used in Bayesian inference and to apply Bayesian inference
to a number of econometric models. Basic concepts and tools introduced include joint, conditional and marginal probability distributions, prior, posterior and predictive
distributions, marginal likelihood and Bayes theorem. Key tools and techniques introduced include Markov chain Monte Carlo (MCMC) techniques, such as the Gibbs and Metropolis Hastings algorithms, for model estimation and model comparison and the estimation of integrals via simulation methods. Throughout the course we will implement Bayesian estimation for various models such as the traditional regression model, panel models and limited dependent variable models using the Matlab programming environment.

Intended learning outcomes

On successful completion of this subject students should be able to:



  • Explain the concepts of joint, conditional and marginal probability density functions and their relevance for Bayesian inference;
  • Derive posterior density functions for common econometric models including the traditional regression model, discrete outcome models and panel models;
  • Explain the relevance of Markov chain Monte Carlo techniques for Bayesian inference;
  • Program Gibbs samplers and Metropolis-Hastings algorithms for a number of models including the traditional regression model, discrete outcome and panel models;
  • Interpret results from Bayesian inference; and
  • Estimate marginal likelihoods for model comparison.

Generic skills

On successful completion of this subject, students should have improved the following generic skills:

  • Evaluation of ideas, views and evidence;
  • Synthesis of ideas, views and evidence;
  • Strategic thinking;
  • Critical thinking;
  • Application of theory to economic policy and business decision making;
  • Summary and interpretation of information;
  • Application of Windows software;
  • Using and designing computer programs;
  • Statistical reasoning;
  • Problem solving skills;
  • Collaborative learning and teamwork;
  • Written communication; and
  • Oral communication.

Last updated: 3 November 2022