–
- Code:
- ECOM-R315/DPE-9315
- Field:
- Econometrics
- Targets:
- Research Master's students PhD students
- Organiser:
- University of Helsinki - Economics
- Instructor:
- Mika Meitz
- Period:
- Period 2
- Format:
- Lecture
- Method:
- Online teaching
- Remote:
- Zoom link can be found in Moodle
- Enrollment:
In case of conflicting information consider the Sisu/Course/Moodle pages the primary source of information.
Aalto, Hanken and UH economics students can enroll through their home university’s SISU. Further instructions are available on the How to enroll? page, also for students from other universities.
If you would like to count the credits towards your degree, please check your curriculum or contact your supervisor or student services for guidance.
Course Sisu and Course page to be published in July
- To access the Moodle course area, use all the features and participate in the activities (assignments, discussions), you must have successfully registered for the course in Sisu and logged in with your UH user ID.
- For more information on how to activate your UH user ID and register for a Moodle course area, click here.
Content
This course moves beyond the standard linear regression model and conventional least squares estimation. Important econometric estimation principles, such as generalized method of moments and maximum likelihood estimation, are covered and the related statistical inference procedures discussed. Basic concepts of simulation-based methods are also introduced. Specifically, the topics covered in the course include
- Generalized method of moments estimation (basic concepts, asymptotic estimation theory, statistical inference)
- Maximum likelihood estimation (basic concepts, asymptotic estimation theory, statistical inference)
- Simulation methods (Monte Carlo simulations, Bootstrap)
Learning outcomes
After the course, the student should know the properties of the estimators introduced and be able to apply them and the related inferential procedures in empirical work. The course should also give a solid foundation for the study of more specialised microeconometric and time series methods.