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Macroeconometrics

Introductory Bayesian Macroeconometrics

Foundations and applications in Bayesian macroeconometric modeling.

17.5h (5 days)

€775 - €1,400

Face-to-face

English

Program date: June 30 - July 4, 2025

Early bird deadline: April 15, 2025

Learn more

Introductory Bayesian Macroeconometrics

Applications for 2025 Summer School programs are now open!

Summer school

Macroeconometrics

Introductory Bayesian Macroeconometrics

Course Overview

This is a course in introductory Bayesian econometrics with a focus on models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and seeing how Bayesian methods work in the familiar context of the regression model. Computational methods are of great importance in modern Bayesian econometrics, and these are discussed in detail.

Subsequently, the course turns to state space models and discusses estimation of several state space models popularly used in macroeconomics. These include time series models where parameters change over time, models with autocorrelated disturbances, and stochastic volatility models.

The models and methods covered in this course are of direct use in many macroeconomic applications. But they also represent the groundwork that underlies popular multivariate macroeconomic models such as Vector Autoregressions (VARs), time-varying parameter VARs (TVP-VARs), factor and Dynamic Stochastic General Equilibrium (DSGE) models.

Faculty

If you’re interested in learning more about Bayesian Macroeconometrics this could be the course for you

This course has been designed for:

Researchers and practitioners working at central banks as well as other private and public institutions
Master’s and PhD students who want to extend their knowledge in macroeconometrics and learn more about frontier research topics

Understand the fundamental concepts of Bayesian theory and its application to econometric models

Upon completion of this course, you will be able to:

Apply Bayesian methods to regression models to see how these techniques function in familiar contexts
Master the computational methods for modern Bayesian econometrics, ensuring practical proficiency
Estimate state space models, including those with time-varying parameters, autocorrelated disturbances, and stochastic volatility
Explore how the covered models and methods can be applied to real-world macroeconomic issues

Program Syllabus for Introductory Bayesian Macroeconometrics course

This introductory course will cover the following topics:

Introduction

Review of the classical linear regression model
Maximum likelihood estimation.
The Bayesian approach to the classical linear regression model
Bayes formula
The likelihood principle
James – Stein result
Ridge regression

Bayesian Estimation of the CLRM

Theil mixed estimator
Prior selection via the marginal data density
The independent Normal-Inverse Gamma prior
Treatment of the error variance
The chi-square, gamma, and inverse gamma distributions
Gibbs sampling
Convergence and mixing
The Natural – Conjugate prior
Marginal data density
The normal diffuse and Jeffrey’s prior

The Generalized Linear Regression Model

Autocorrelation and Heteroskedasticity
Stochastic volatility models
Metropolis Hastings algorithms
Independence Metropolis
Random Walk Metropolis

Linear and Gaussian State Space Models

Forward filtering/backward sampling algorithms
Carter-Kohn algorithm models with time varying coefficients

Non-linear Non-Gaussian State Space Models

The Kim, Shepard, and Chib algorithm for stochastic volatility models
Sequential Monte Carlo methods

List of References

Here is a list of texts that may help you to prepare for this course.

Articles and Books

Koop, G. (2003). Bayesian Econometrics, published by Wiley.
Koop, G. (2016). Bayesian Methods for Fat Data.
Chan, J., Koop, G., Poirier, D. and Tobias, J. (2019). Bayesian Econometric Methods, second edition, published by Cambridge University Press.

Software / Hardware

MATLAB will be used during the course. Participants will receive a free, time-limited MATLAB license before the program starts
Please install MATLAB on your computer before the course begins for use in practical sessions
Participants must bring their own laptop to participate in the practical sessions

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

Admissions and Requirements

All BSE Summer School applicants must meet the entrance requirements.

Program date: June 30 - July 4, 2025

Early bird deadline: April 15, 2025

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Introduction to Bayesian Macroeconometrics course

Participants must have a basic knowledge of time series econometrics

Schedule

Here is your schedule for this edition of BSE Macroeconometrics Summer School Introductory Bayesian Macroeconometrics course.

Time

30

mon

1

tue

2

wed

3

thu

4

fri

09:00 - 11:00

Lecture

14:30 - 16:00

Practical

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Fees

Multiple course discounts are available; see more information about available discounts. Fees for other courses listed in other Summer School programs may vary.

Course

Introduction to Time Series Analysis

Introductory Bayesian Macroeconometrics

Time Series Models for Macroeconomic Analysis I

High-Dimensional Time Series Models

Time Series Models for Macroeconomic Analysis II

Bayesian Estimation of RANK and HANK Business Cycle Models

Introduction to Nowcasting and Forecasting

Modality

Online

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

775€

1,400€

Reduced Fee*

475€

775€

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

FAQ

Here are some commonly asked questions by participants. Any further queries, please contact our Admissions Team.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.