Bayesian Methods in Economics and Finance

Bertinoro, August 26-30, 2019



Gaetano Carmeci
Università di Trieste
Dipartimento di Scienze Economiche, Aziendali, Matematiche e Statistiche “B. de Finetti” (DEAMS)
Via Tigor 22
34124 Trieste
tel. +39 0405587100


Gaetano Carmeci , University of Trieste
Roberto Casarin , University of Venice, Italy Ca' Foscari
Matteo Ciccarelli , ECB

Basic Requirements

Intermediate knowledge of econometrics


Reference textbook for the course:

  • Berger, J. O. (1985), Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics (Second ed.). Springer Verlag.
  • Gilks, W. R., S. Richardson and D. J. Spiegelhalter (1996), Markov chain Monte Carlo in practice, London: Chapman and Hall.
  • Greenberg, E. (2008), Introduction to Bayesian Econometrics, Cambridge University Press.
  • Koop, G., Dale J. P., Tobias, J. L. (2007.), Bayesian Econometric Methods, Cambridge University Press.
  • Kroese, D.P. and J. Chan (2014), Statistical Modeling and Computation, Springer Verlag.
  • Liu, J. (2001), Monte Carlo Strategies in Scientific Computing, Springer Verlag.
  • Robert, C. P. (2001), The Bayesian Choice – A Decision-Theoretic Motivation (second ed.). Springer- Verlag.
  • Zellner, A. (1971), Introduction to Bayesian Inference in Econometrics, Wiley and Sons.

More references in the public attachment section.

Schedule of the course:

The course is an introduction on Bayesian Inference, starting from first principles and covering topics of interest for applied econometricians in economics and finance. The course is addressed to students without previous knowledge of Bayesian Econometrics. The methods introduced in the lectures will be illustrated with hands-on applications in MATLAB based on reasoned statistical and economic examples.

A. Fundamentals of Bayesian Statistics

B. Bayesian computation

  • Monte Carlo simulation
  • Markov chains
  • Markov Chain Monte Carlo methods (Gibbs sampler and Metropolis-Hastings   algorithm)
  1. Comparing performance
  2. Checking convergence
  3. Optimal scaling
  • An introduction to advanced MCMC and other simulation methods

C. Bayesian methods for regression models

  • Normal linear regression models
  1. Standard LRM with spherical and non-spherical errors
  1. Hierarchical models
    1. Seemingly Unrelated Regression models
    2. Panel data models
  1. Introduction to time-varying parameter and stochastic volatility models


  • Bayesian VAR models
  1. Basic models
    1. Likelihood, priors and posterior derivation
    2. Uses of VAR models: Forecasting and Structural analysis
  2. Bayesian VAR Lasso
    1. Elastic net
    2. Adaptive Lasso
    3. Doubly adaptive elastic net
  1. Bayesian VAR nonparametric Lasso


Participants will use their laptops with MATLAB already installed on them. 


Program is conditional to the recruitment of a minimum of 15 participants


Venue and timetables

The course will last one week and will be held in the University Residential Centre, Via Frangipane 6, 47032 Bertinoro (FC).

Participants will be hosted in the Centre guest quarters, (as an exception, in case of reduced availability of rooms in the Centre, they will be accommodated in local hotels).

Lectures and tutorials will be in English, with the following schedule:

Monday to Thursday lectures: 9.00-13.00, 15.30-19.30.

Friday: lectures: 9.00-13.00.


Fees and Enrollment

  •  Students, new graduated students, PhD students and temporary university staff: 650€
  •  University staff: 800€ 
  •  Others: 2300€

Fee includes: accommodation (usually in doube room with breakfast and lunch starting from Sunday evening.

Partecipants who wish to attend two or three Courses, are allowed the following reduced fees per Course

  •  Students, new graduated students, PhD students and temporary university staff: 550€ per Course
  •  University staff: 700€ per Course
  •  Others: 2000€ per Course