Bayesian Methods in Economics and Finance


If the conditions of the ongoing COVID 19 pandemic allow it, the course will  be also delivered in presence. Partecipants may express thier preference upon acceptance

Venice,  August 30- September 3, 2021



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, European Central Bank, DG Economics, Head of Forecasting and Policy Modelling Division
Federico Bassetti, Politecnico di Milano


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 requires full-time attendance, and participation is not compatible with other jobs at the same time (e.g. preparation of other exams). Lectures and tutorials will be in English, with the following schedule (provisional):

  • Monday to Friday: lectures: 9.00-13.00, 15.00-18.00.

The course will be held in the Campus Economico San Giobbe at Università Ca’ Foscari, Venezia, Italy. Address: Dipartimento di Scienze Economiche - S. Giobbe, 873 - 30121 Venezia.
Participants will be hosted in the Ca' Foscari Residence in Santa Marta (as an exception, in case of reduced availability of rooms they will be accommodated in local hotels).


Fees and Enrollment

* Online Course

  • Students, new graduated students, PhD students and temporary university staff: 340 euro 
  • University staff: 450 euro
  • Others: 1500 euro

* Course in Venice

  •  Students, new graduated students, PhD students and temporary university staff: 690€
  •  University staff: 800€ 
  •  Others: 2300€

Fee includes: accommodation breakfast and lunch (starting from Sunday evening).
If participants do not need any accomodation during the course, please send an email to to request a reduced fee and we will apply a cost reduction of 100€.

In case of enrollment in two or more courses, for a maximum of three, Student and Staff participants are entitled to a discount of 100 euros on each course. Other participants are entitled to a discount of 300 euros on each course.

* Fees for  Master and PhD students from Ca' Foscari University of Venice: 30€ 

* Fee for students from Ca' Foscari University of Venice does not include accommodation.


Renounce and refund:

To submit a renounce request, please send an email to

You can give up immediately after the notification of acceptance or later.
After the payment, you can submit your renounce up to one week (7 days) from the beginning of the course (within the terms for refund) and ask for a refund with motivated reasons (health reasons to be documented, for study purposes or personal reasons). We will refund your fee with a deduction for administrative and organization costs: 
150 Euro for the course in presence, 100 Euro for online course.
Over the terms for refund (less of 7 days from the beginning of the course) you need to motivate your request (as indicated above), which will be submitted to SIdE President.


Important dates:

Application Deadline: April 30th, 2021

Notification of acceptance will be posted by the 7th May, 2021

Deadline for Fee Payment is May 30th, 2021