Macroeconometrics Summer School, Barcelona Graduate School of Economics

Barcelona Graduate School of Economics Spain

For more information about Macroeconometrics Summer School at Barcelona Graduate School of Economics, please visit the webpage using the button above.

The award
Summer School

How long you will study
find out

Domestic course fees
find out

How you will study

Course starts
June, July

International course fees
EUR 1275 per total

All study options

About Macroeconometrics Summer School at Barcelona Graduate School of Economics

Macroeconometrics is an important area of research in economics. Time series methods for empirical macroeconomics have become very popular and widely used in the academia as well as in public and private institutions.

The goal of the Barcelona GSE Macroeconometrics Summer School is to offer courses covering a wide range of topics in macroeconometrics. The courses have the following objectives:

  • To provide students with knowledge of a set of modern time series methods necessary for empirical research in macroeconomics. 
  • To present a variety of empirical applications in macroeconomics. 
  • To survey some of the recent developments in macroeconometrics. 

In general, the courses will have an empirical orientation. Although econometric theory will have a central role, special attention will be paid to the applications and data. The level of the courses should be comparable to those taught in the Barcelona GSE master programs. 

Course list for 2020

Week 1 (June 29 - July 3, 2020)

  • Bayesian Methods for DSGE Models
    Instructor: Kristoffer Nimark (Cornell University)
  • Modeling Non-stationary and Non-linear Time Series
    Instructor: Laura Mayoral (IAE-CSIC and Barcelona GSE) and Gabriel Pérez-Quirós (Bank of Spain)
  • Time Series Methods for Macroeconomic Analysis
    Instructor: Luca Gambetti (UAB and Barcelona GSE)

Week 2 (July 6-10, 2020)

  • Bayesian Time Series Methods: Introductory
    Instructor: Gary Koop (University of Strathclyde)
  • High-Dimensional Time Series for Macroeconomic Analysis I
    Instructors: Luca Sala (Bocconi University)

Week 3 (July 13-17, 2020)

  • Bayesian Time Series Methods: Advanced
    Instructor: Andrea Carriero (Queen Mary University of London)
  • High-Dimensional Time Series for 

Study options for this course

  • The award How you will study How long you will study Course starts Domestic course fees International course fees
  • The awardSummer SchoolHow you will studyFull-timeHow long you will study find out
    Course startsJune, JulyDomestic course fees find outInternational course feesEUR 1275 per total

Notes about fees for this course

The price of each course includes 10 hours of lecture time and between 5 and 7.5 hours of practical time. Multiple course discounts are available. Fees for courses in other Summer School programs may vary.

Courses including 10 Lecture Hours and 5 hours of practical time are priced as follows:

  • Regular Fee: 1275€
  • Reduced Fee*: 700€

Courses including 10 Lecture Hours and 7.5 hours of practical time are priced as follows:

  • Regular Fee: 1350€
  • Reduced Fee*: 725€

* Reduced Fee applies to PhD/Masters students, including Barcelona GSE students and alumni. See more information about available discounts or request a personalized discount quote by email.

Entry requirements

The Barcelona GSE Summer School courses are taught at a high academic standard. A strong background in Economics or related fields is a must. Holding a postgraduate degree, or currently studying in a Master's or PhD program, and/or having related professional experience, are key factors that make an application successful.

As part of your online application, you will be required to upload the following documents:

  • Up-to-date CV
  • Proof or reference for each course fee discount that you are entitled to (if applicable)

Courses are taught entirely in English. While no language certificate is required at the moment of application, participants are expected to have enough command of the language to follow and benefit from the course without causing disruption to the development of the class.

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