Excerpt from course description

Bayesian Econometrics

Introduction

Please note that this course will be revised before it is offered again.

Course content

1. Concepts for Bayesian Inference

  • Bayesian inference
  • Criteria for evaluating statistical procedures
  • Probability: objective or subjective

2. Numerical Methods for Bayesian Inference

  • Need for numerical integration
  • Deterministic integration
  • Monte Carlo integration

3. Bayesian Inference for Regression Analysis

  • Regression with non-informative prior
  • Regression with conjugate prior
  • Partially linear model
  • Regression with non-conjugate prior
  • Heteroskedastic errors
  • Autocorrelated errors
  • IID student errors

4. Bayesian Inference for vector autoregressive models

  • Unrestricted VAR and multivariate regression models
  • Posterior with NIP
  • Posterior with informative prior
  • The Minnesota prior
  • Restricted VAR and SURE models

5. Bayesian Inference for volatility models

  • ARCH models
  • Stochastic volatility models

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.