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