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Macroeconometrics

Macroeconomic Forecasting with Machine Learning

An introduction to machine learning methods specifically designed for economic time-series forecasting.

17.5h (5 days)

€1,399

Face-to-face

English

Program date: July 13-17, 2026

Early bird deadline: April 15, 2026

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Macroeconometrics

Macroeconomic Forecasting with Machine Learning

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Who is this Course for?

Macroeconomic Forecasting with Machine Learning

Course overview

This course introduces machine learning methods specifically designed for economic time-series forecasting. The course addresses the growing need for sophisticated forecasting tools that can handle high-dimensional economic data while maintaining interpretability for policy and business decisions.

The course bridges the gap between modern machine learning techniques and practical economic forecasting applications, focusing on methods that have demonstrated superior performance in academic research and real-world implementations.

Participants will learn to overcome limitations of traditional econometric approaches when dealing with large datasets, mixed-frequency data, and complex nonlinear relationships in economic variables.

Upon completion, participants will be equipped to build and deploy forecasting systems that deliver measurable performance improvements over conventional methods. The course emphasizes both predictive power and economic interpretability, ensuring models remain useful for decision-making contexts.

Participants are not required to have prior experience in advanced Machine learning.

Faculty

Who is this course for?

This course is useful for:

PhD students and academic researchers incorporating modern prediction methods into their research toolkit, particularly those working on forecasting, asset pricing, or macroeconomic applications
Central bank economists and policy researchers responsible for implementing or evaluating nowcasting and forecasting systems for policy decision-making
Quantitative analysts and finance professionals seeking to enhance forecasting capabilities beyond traditional factor models, particularly those in asset management, risk management, or trading roles.
Private sector economists at consulting firms, corporations, and financial institutions requiring accurate forecasting for strategic planning and business decisions
Data scientists transitioning into economic and financial applications who need to understand the unique challenges and best practices for economic time series data.

Learning outcomes

By the end of the course, participants will be able to:

Apply evidence-based forecasting techniques supported by published research to improve real-world prediction performance
Implement practical forecasting systems using authentic datasets, replicating the methods used by central banks and financial institutions
Balance predictive accuracy with economic interpretation, ensuring that machine learning models provide meaningful insights for policy and decision-making
Compare and evaluate methods across traditional econometric and modern machine learning approaches on identical datasets
Work with real-world datasets from institutions such as the Federal Reserve, ECB, and Bank of England, gaining hands-on experience with high-quality financial and economic data
Integrate interdisciplinary knowledge, connecting advances in computer science with economic theory and institutional practice

Key topics for Macroeconomic Forecasting with Machine Learning course

Here is a brief outline of what we will cover.

Day 1: Regularized Methods and High-Dimensional Forecasting

Challenges of high-dimensional economic data
Bias-variance tradeoffs in forecasting contexts
Shrinkage and variable selection techniques
Time series validation and cross-validation approaches
Economic interpretation of regularized models

Day 2: Ensemble and Tree-Based Methods

Tree-based algorithms for economic time series
Handling irregular data frequencies and missing observations
Variable importance and economic insights
Boosting approaches for economic applications
Model combination and ensemble strategies

Day 3: Neural Networks and Modern Deep Learning

When and why neural networks excel for economic forecasting
Architecture considerations for financial and macroeconomic data
Recurrent models for sequential economic data
Natural language processing for economic text analysis
Policy communication and sentiment extraction

Day 4: Large-Scale Multivariate Models

High-dimensional vector autoregressions
Factor models and dimensionality reduction techniques
Alternative approaches to capturing common dynamics
Cross-sectional and temporal dependency modeling
Multi-country and multi-sector frameworks

Day 5: Comparative Applications and Performance Evaluation

Comprehensive case studies across multiple economic domains
Direct method comparisons on identical datasets
Inflation forecasting across competing approaches
Asset return prediction methods
Real-time nowcasting applications
Model selection and validation best practices

List of References

Core Texts

Core references include foundational texts in statistical learning, alongside recent research published in leading econometrics and finance journals such as the Journal of Econometrics, Journal of Business & Economic Statistics, Review of Financial Studies, and Journal of Applied Econometrics.

The course also draws on working papers from major central banks and survey articles covering recent advances in machine learning for time-series forecasting.

A comprehensive reading list will be provided to all enrolled participants.

Software / Hardware

Software:

R, Python, or MATLAB (participant’s choice)
Standard machine learning libraries and packages
Access to statistical computing environment

Hardware:

Personal laptop capable of running statistical software
Minimum 8GB RAM recommended for computational exercises

Note

All necessary code examples and datasets will be provided
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

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

Participants must check they are eligible to take the course before applying.

Program date: July 13-17, 2026

Early bird deadline: April 15, 2026

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 Macroeconomic Forecasting with Machine Learning

Required knowledge:

Solid foundation in econometrics and regression analysis (equivalent to third year undergraduate degree)
Familiarity with basic time series and forecasting concepts
Understanding of statistical inference fundamentals

Technical skills:

Programming experience in Python, or MATLAB for laboratory sessions
Ability to work with economic datasets and perform statistical analysis

Schedule

Here is your schedule for this edition of BSE Macroeconometrics Summer School Nowcasting and Forecasting course.

Time

13

mon

14

tue

15

wed

16

thu

17

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

Macroeconomic Forecasting with Machine Learning

Introduction to Nowcasting and Forecasting

Introductory Bayesian Macroeconometrics

Bayesian Estimation of RANK and HANK Business Cycle Models

High-Dimensional Time Series Models

Modeling and Forecasting Macroeconomic Risk

Time Series Models for Macroeconomic Analysis I

Time Series Models for Macroeconomic Analysis II

Modality

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

1,399€

Reduced Fee*

799€

* 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?

Unfortunately, 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.

Can I take more than one course?

Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Cancelation and Refund Policy

Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

Yes, a social dinner is held once a week for all participants, it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine this program with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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