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B0369 - BAYESIAN ECONOMETRICS

Anno Accademico 2022/2023

                
                        Docente:
                        Andrea Carriero
                    
                        Crediti formativi:
                        6
                    
                        SSD:
                        SECS-P/05
                    
                        Lingua di insegnamento:
                        Inglese
                    
                        Moduli:
                        
                            Andrea Carriero
                            (Modulo 1)
                        
                            Silvia Sarpietro
                            (Modulo 2)
                        
                        Modalità didattica:
                        
                                    Convenzionale - Lezioni in presenza (Modulo 1)
                                
                                    Convenzionale - Lezioni in presenza (Modulo 2)
                                
                            Campus:
                            Bologna
                        
                            Corso:
                            Laurea Magistrale in
                            Economics (cod. 8408)

                            Risorse didattiche su Virtuale
                        
                                        Orario delle lezioni (Modulo 1)
                                    
dal 02/03/2023 al 16/03/2023

                                        Orario delle lezioni (Modulo 2)
                                    
dal 13/02/2023 al 01/03/2023

Conoscenze e abilità da conseguire

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.

Contenuti

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

Testi/Bibliografia

Bayesian Econometrics, by Gary Koop

Contemporary Bayesian Econometrics and Statistics, by John Geweke

Modalità di verifica e valutazione dell'apprendimento

Esame scritto a libro chiuso.

<18 insufficiente

18-23 sufficiente

24-27 buono

28-30 ottimo

30 e lode: eccellente

Strumenti a supporto della didattica

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.

Orario di ricevimento

Consulta il sito web di Andrea Carriero

Consulta il sito web di Silvia Sarpietro