Econometric Methods: Theory and Practice

Big Data, regression methods, forecasting, value-at-risk; your professional environment is becoming more and more quantitative. If you want to enhance your professional skills, and update your knowledge, this course is for you.

This course covers all the basics econometric methods and tools for analyzing both cross-sectional and time-series data. This course addresses both the theory and practice of econometrics in a wide range of settings, from Economics, to Business, Marketing and Finance. This course will also allow you to gain practice in programming with either R, Python, or MATLAB.

The practical assignments will cover simulation-based econometric methods, numerical estimation methods, and solving practical relevant problems using real data sets.

Fee:                    € 4,600. Reduced fee is € 3,995 for bookings made before June 30th 2020.
Audience:            Professionals in the financial industry or at corporates, who want to strengthen their skills in quantitative methods and tools.
Duration:            7 day course in August  2020. The course will only go through if there is a sufficient number of applications.
Calendar:            Wednesday, Aug 19 -- Friday, Aug 30. Classes take place on the days mentioned below:

Econometric Methods in 2 Weeks


Monday
(afternoon)
Tuesday
(afternoon)
Wednesday
(afternoon)
Thursday
(afternoon)
Friday
(afternoon)
Week 1
N/A
N/A

Lecture
Computer lab
Lecture
Computer lab
Tutorial
Computer lab
Week 2
Lecture
Computer lab
Lecture
Computer lab
Tutorial
Computer lab
N/AFinal Class

Coordinator:   F. Blasques
Lecturer:        F. Blasques
Tutor:             A. Duplinskiy
Content:        

• Regression methods for cross-sectional data
• Time-series models for stationary and non-stationary data
• Forecasting, impulse response functions and policy analysis
• Financial econometric models for conditional volatility
• Value-at-risk and other financial risk metrics

Requirements:  Participants should be familiar with basic probability and statistics. Some knowledge of introductory econometrics and time-series analysis is also recommended. 

Grading:            To successfully complete this course, attendants must take part in all classes and deliver the mandatory assignment.