- Docente: Andrea Carriero
- Credits: 6
- SSD: SECS-P/05
- Language: English
- Moduli: Andrea Carriero (Modulo 1) Silvia Sarpietro (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Economics (cod. 8408)
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from Mar 02, 2023 to Mar 16, 2023
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from Feb 13, 2023 to Mar 01, 2023
Learning outcomes
The goal of this course is to introduce students to the basic tools of Bayesian analysis, and to apply them to make inference in the linear regression model. By the end of the course students will: 1) be familiar with the main steps of Bayesian inference; 2) be able to elicit an appropriate prior distribution; 3) be able to build a posterior simulator; 4) be able to estimate classical and general linear regression models using Bayesian techniques.
Course contents
1. Introduction to Bayesian methods.
2. Specifications of prior distributions: conjugate, improper, informative, flat, and Jeffrey’s priors.
3. Large-sample Bayesian Inference, and relation with the frequentist approach.
4. Admissibility. James-Stein estimator and Empirical Bayes, Lasso and Ridge regression.
5. Numerical Integration and Posterior Simulators: Markov Chain Monte Carlo methods (Gibbs sampler and Metropolis-Hastings algorithm), and an introduction to other simulation methods.
6. Introduction to Bayesian estimation of the linear regression model. Maximum likelihood estimation, the likelihood principle, Theil mixed estimation.
7. Priors for the Linear Regression model: The independent normal-gamma prior and conjugate normal-gamma prior.
8. Models with heteroskedasticity and autocorrelation.
9. State-space models. Models with drifting coefficients and volatilities.
10. Introduction to Bayesian Vector Autoregression
Readings/Bibliography
Bayesian Econometrics, by Gary Koop
Contemporary Bayesian Econometrics and Statistics, by John Geweke
Assessment methods
Closed book examination.
<18 fail
18-23 pass
24-27 merit
28-30 distinction
30 e lode:excellent
Teaching tools
Dedicated page on the VIRTUALE platform containing:
· News and updated information
· Lectures slides
· MATLAB code
Software MATLAB: can be installed on students' personal computers (CAMPUS license) and is available at the Computer Lab of the School of Economics and Management.
Office hours
See the website of Andrea Carriero
See the website of Silvia Sarpietro