316-407 Bayesian Econometrics

Students may not gain credit for both 316-407 Bayesian Econometrics and 316-672 Bayesian Econometrics.

316-470 Econometric Techniques or equivalent.

Basic tools and characteristics of Bayesian inference and the application of Bayesian inference to a number of econometric models are considered. The tools and characteristics will include joint, conditional and marginal probability distributions, prior, posterior and predictive distributions, Bayes theorem, representing uncertain information, and the estimation of moments and other integrals via Markov chain Monte Carlo techniques. The econometric models will include the traditional regression model, the seemingly unrelated regressions model, probit and tobit models and some time-series models.

• High level of development: evaluation of data and other information; synthesis of data and other information; critical thinking; interpretation and analysis; use of computer software; statistical reasoning; problem solving; collaborative learning; written communication; oral communication.

• Moderate level of development: receptiveness to alternative ideas; application of theory to practice.

• Some level of development: accessing data and other information from a range of sources.

A 2-hour end-of-semester examination (60%) and class assignments up to 4000 words in total (40%).