Advanced Econometrics 1 (5 cr)

Code:
ECOM-R314/DPE-9314
Field:
Econometrics
Targets:
Research Master's students PhD students
Organiser:
University of Helsinki - Economics
Instructor:
Mika Meitz
Period:
Period 1
Format:
Lecture
Method:
Online teaching
Remote:
Zoom link can be found in the Moodle
Enrollment:

In case of conflicting information consider the Sisu/Course/Moodle pages the primary source of information.

Aalto and Hanken economics students can enroll in their home university’s SISU! Further instructions can be found on the How to enroll page, also for other students.

Before taking and completing the course make sure that the credits can be counted towards your degree at your home university by checking which courses are included in your curriculum or by contacting your home university’s student/learning services.

Please note that there is a different code for UH PhD students: DPE-9314

  • 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.

This course introduces the basic methods used in the linear regression analysis of economic variables. The classical finite sample theory and asymptotic analysis of the linear regression model as well as the necessary methodological tools required for these topics are covered. Specifically, the topics covered in the course include

  • Classical finite sample theory in the linear regression model 
  • The basics of asymptotic theory 
  • Asymptotic theory in the linear regression model 
  • Autocorrelation, heteroskedasticity and dynamic regressors 
  • Specification tests 
  • Omitted variables, instrumental variables and the two-stage least squares estimator (2SLS)

After the course, the student should

  • Know the main properties and limitations of the linear regression model 
  • Be familiar with the basics of asymptotic analysis 
  • Be able to employ the linear regression model and related inferential methods in empirical research