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Online postgraduate courses
Bayesian Methods in Econometrics
This course will provide an introduction to simulation-based methods that are commonly used in microeconometrics. The emphasis will be Bayesian, although we will also contrast posterior analysis with maximum likelihood estimation. Despite the technical content, the course begins with an introduction to the Bayesian paradigm and introduces key concepts and vernacular
This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.
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- 10 Credits
- 100 hours of study
- 15 contact hours
- 85 hours for private study
Faculty
Structure
Structure
Bayesian Methods in Econometrics is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.
The total number of contact hours is 15 hours. This leaves 85 hours for private study.
Lectures
This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day.
During their private study, students should read the course literature, work on practical exercises, and write a project report for the examination.
The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact edu@timberlake.co.uk.
Python
R
Module Programme
Introduction to Bayesian statistics and its differences versus the classical/frequentist approach
- Advantages of the Bayesian approach over the frequentist approach: general theory andsimple examples
- Bayes’ rule: prior density, likelihood, posterior density and posterior density kernel
- Informative versus non-informative priors
- Bayesian analysis of model with Bernoulli/binomial distribution under uniform prior
Prior specification in simple econometric models, Gibbs sampling
- Conjugate priors
- Bayesian analysis of model with Bernoulli/binomial distribution under conjugate (beta) priordistribution
- Bayesian analysis of model with Geometric distribution
- Proper versus improper priors
- Bayesian analysis of model with normal distribution with known and unknown variance(under normal and flat prior distributions)
- Markov chain Monte Carlo (MCMC) methods: burn-in, Gibbs sampling method
Metropolis-Hastings algorithms (methods and applications in econometric models)
- Markov chain Monte Carlo (MCMC) methods: random walk Metropolis(-Hastings) method,candidate distribution, diagnostic checks, acceptance percentage
- Bayesian estimation of (G)ARCH models
- Bayesian estimation of Poisson regression model
- Markov chain Monte Carlo (MCMC) methods: independence chain Metropolis-Hastingsmethod, candidate distribution, diagnostic checks, acceptance percentage
Posterior model probabilities, normal linear regression model, Bayesian Model Averaging for forecast combination of autoregressive models
- Posterior model probabilities: posterior odds ratio, prior odds ratio, Bayes factor, marginallikelihood
- Monte Carlo methods: importance sampling method, acceptance-rejection method
- Bayesian analysis of model with normal distribution with unknown variance under flat prior:Student-t marginal posterior of the mean
- Bayesian analysis of normal linear regression model under flat prior: Student-t marginalposterior of the coefficients, symmetry and differences between Bayesian andclassical/frequentist results
- Savage-Dickey density ratio for computing the Bayes factor of nested models
- Posterior predictive density in autoregressive (AR) models
- Bayesian Model Averaging (BMA) for forecast combination
- Jeffreys-Lindley-Bartlett paradox and the need for proper priors in BMA
Gibbs sampling with data augmentation (method and applications in econometric models), hierarchical prior, Jeffreys prior, numerical standard errors
- Markov chain Monte Carlo (MCMC) methods: Gibbs sampling with data augmentation
- Bayesian estimation of a mixture model with known/fixed number of components
- Bayesian estimation of a mixture model with unknown/flexible number of components:hierarchical priors, sparse finite mixture
- Bayesian estimation of probit, tobit models
- Jeffreys prior
- Numerical precision of simulation results: numerical standard errors, relative numericalefficiency
Prerequisites
English Language Requirements
Both Programmes are open to applicants anywhere in the world. We may ask applicants to provide a recognised English language qualification, dependent upon their nationality and where they have studied/worked previously.
The requirement is an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We will also consider other English language qualifications. If their score is below our requirements, they may be eligible for one of Lancaster University's pre-sessional English language programmes.
Academic Requirements
Applicants to the Postgraduate Certificate of Achievement, Postgraduate Certificate, Postgraduate Diploma or full MSc in either programme require either an upper second-class degree in economics, econometrics or related subjects.
Learning Outcomes
- The basis and motivation for Bayesian Econometrics
- The distinction between inference and estimation in Bayesian versus classical methods
- How to construct posterior distributions of unknown parameters
- The application of Bayesian methods to problems in microeconometrics
- Communicate and present complex arguments in oral and written form with clarity
- Appreciate instances where the application of Bayesian methods is appropriate
- Work with R and/or python to operationalize Bayesian methods
Frequently Asked Questions
All sessions are conducted live and online at a scheduled time, but are also recorded. Students may attend live and watch the recordings back to recap the material or watch the recordings only if unable to attend live. We always advise students to attend live where possible as this will allow them the best opportunity to engage with the content and ask the lecturer's questions.
All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.
No, all necessary software is provided to students.
All courses are recorded and available on the LUMS internet platform throughout the current academic year. They can therefore be viewed 24 hours a day.
Timberlake Consultants and Lancaster University Management School (LUMS) Economics department have a longstanding partnership; combining 40+ years of industry expertise with over 50 years of academic excellence. We are delighted to build on this with our micro-credential postgraduate courses.
Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education
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Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education