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Macroeconometrics

Introduction to Forecasting With Machine Learning

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

20h (10 days)

€1,375-€795

Online

English

Next edition: March 8-19, 2027

Early bird deadline: January 20, 2027

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Macroeconometrics

Introduction to Forecasting With Machine Learning

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Who is this Course for?

Introduction to Forecasting With Machine Learning

Course overview

Forecasting is central to decision-making in economics, finance, central banking, and business. The rapid growth of machine learning methods has transformed the forecasting landscape by providing tools capable of handling large datasets, nonlinear relationships, and unstructured information such as text.

At the same time, economists and financial analysts increasingly require a rigorous understanding of when machine learning methods outperform traditional econometric approaches and how these methods can be implemented in practice.

This course provides participants with both the theoretical foundations and practical skills needed to apply modern forecasting methods to real-world economic and financial problems.

Lectures and computer labs alternate throughout the program, providing a balance between conceptual understanding and practical implementation. Five lecture sessions are complemented by five hands-on computer laboratories covering all major forecasting methods introduced in the course.

Faculty

Discover what makes this forecasting with machine learning course exceptional

1

Expert-led: Taught by Professor Dimitris Korobilis, a leading researcher in Bayesian econometrics, forecasting, and machine learning.

2

Rigorous and modern: Combines modern machine learning techniques with rigorous econometric foundations.

3

Focused on economic forecasting: Examines machine learning applications specifically in economic and financial forecasting rather than generic machine learning applications.

4

Theory and practice:Gives equal emphasis to theory and practical implementation through hands-on computer labs.

5

Frontier topics:Covers established forecasting methods alongside topics such as deep learning, text analytics, and large language models.

6

Real-world data: Uses real-world macroeconomic and financial datasets.

7

Take-home resources: Participants receive MATLAB code and supplementary Python materials for continued learning after the course.

8

Broadly relevant: Designed for both academic researchers and industry practitioners.

Who is this course for?

The course is designed for:

Graduate students in economics, finance, econometrics, statistics, and data science.
PhD students and early-career researchers interested in forecasting and machine learning.
Economists working in central banks, government institutions, international organizations, and policy agencies.
Financial analysts, risk managers, and quantitative practitioners seeking to incorporate modern forecasting tools into their workflows.
Professionals interested in applying machine learning methods to economic and financial data.

Learning outcomes

Upon completion of the course, participants will be able to:

Understand the principles of forecast construction and evaluation
Distinguish between traditional econometric forecasting methods and machine learning approaches
Apply penalized regression techniques such as Ridge and LASSO
Implement nonlinear forecasting methods, including random forests, gradient boosting, and kernel-based models
Build and evaluate Bayesian VARs, factor models, and high-dimensional forecasting systems
Understand the role of neural networks and deep learning in economic and financial forecasting
Incorporate text data and large language models into forecasting exercises
Conduct forecasting experiments using real macroeconomic and financial datasets
Critically assess the strengths and limitations of machine learning methods in applied forecasting environments

Key topics for Introduction to Forecasting with Machine Learning

Take a look at the themes covered during this course.

Forecasting Fundamentals and the Machine Learning Perspective

Classical time-series forecasting
Forecast evaluation
Bias-variance trade-off
From econometrics to machine learning
Ridge and LASSO methods

Nonlinear and Nonparametric Methods

Nonlinear forecasting models
Decision trees
Random forests
Gradient boosting
Kernel methods and Gaussian processes

Vector Autoregressions and Factor Models

VAR fundamentals
Bayesian VARs and shrinkage methods
High-dimensional forecasting
Factor models and FAVARs

Deep Learning for Economics and Finance

Neural network foundations
Deep learning architectures
Recurrent and sequence models
Applications to macroeconomic and financial forecasting

Text Data, Language Models, and Frontiers

Text as economic data
Topic models
Latent semantic analysis
Transformer models and large language models
Text-based forecasting and current research frontiers

Computer Labs

Hands-on forecasting exercises using macroeconomic and financial datasets. Topics include Ridge and LASSO forecasting, random forests, Bayesian VARs, neural networks, sentiment analysis, topic modelling, and text-augmented forecasting. MATLAB is used throughout, with supplementary Python materials provided

Course Materials and Software

MATLAB License

Every participant will receive a time-limited personal free MATLAB license before the course starts. You’ll need to install it on your own computer for practical sessions
Additional materials will be provided, and instructors will be available to discuss your research ideas and projects throughout the course

Why should you attend BSE Executive Education courses?

All BSE Executive Education courses are taught to the same high standard as our Master’s programs.

1

Network with like-minded peers from around the world

2

Short courses allow you to learn without a big time commitment

3

Try something new and expand your knowledge and career prospects, or advance your thesis

Admissions

If you want to apply for this Nowcasting and Forecasting course, ensure you meet the criteria below.

Next edition: March 8-19, 2027

Early bird deadline: January 20, 2027

Requirements

Candidates are assessed on an individual basis according to their professional or academic background
Students must have their own laptop or desktop computer and a good Internet connection to be able to follow and fully benefit from the course

Requirements for Introduction to Forecasting with Machine Learning

Participants should have prior exposure to graduate-level econometrics and familiarity with MATLAB or Python

Course Schedule

The times listed are Central European Time (CET). Compare with your time zone on time.is

Instructors, topics, and schedules are subject to change.

Week 1

Time

1
Mon

2
Tue

3
Wed

4
Thu

5
Fri

14:30-16:30

Lecture

Practical

Lecture

Practical

Lecture

Week 2

Time

1
Mon

2
Tue

3
Wed

4
Thu

5
Fri

14:30-16:30

Practical

Lecture

Practical

Lecture

Practical

Certificate and Fees

Certificate

Participants who attend at least 80% of the course will receive a Certificate of Attendance free of charge. Participants will not be graded or assessed during the course.

Fees

A 10% discount applies when the confirmation payment is completed on or before the announced Early Bird deadline.

Multiple course discounts are available. Find out more information in our Fees and Discounts pdf.

Fees for courses in other Executive Education programs may vary.

Course

Introduction to Forecasting With Machine Learning

Modality

Online

Total Hours

20

ECTS

0

Regular Fee

€1,375

Reduced Fee*

€795

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

FAQ

Need more information? Check out our most frequently asked questions.

See the full Executive Education calendar

The full calendar is available to view here.

Are the sessions recorded?

Sessions will be recorded and videos will be available for a month once the course has finished.

How much does each Executive Education 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 Executive Education courses. See more information about available discounts or request a personalized discount quote by email.