Advanced Econometrics 1 (5 cr)

Research Master's students PhD students
University of Helsinki - Economics
Mika Meitz
Period 1
Remote teaching
Zoom link can be found in the Moodle

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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)
  • Completion method: remote teaching
  • Schedule: can be found in Course Page and Sisu
  • Study materials: can be found in Moodle
    • For some courses, it is enough to register in Sisu and you can access directly the Moodle area, please note, however, that it may take up to two hours after registration to enter the Moodle area.
    • Log in with your UH username to be able to use all the features of the course workspace
    • More tips for enrolling in Moodle can be found here
  • Self study material to be studied before the course starts

Please register for the course in the UH Sisu with your UH username. Further instructions can be found here.

Aalto University Students
  • Code: ECON-L4100
  • Target groups: PhD / rMSc
  • Credit points: 5
  • Credit transfer: apply for substitution in Sisu
Hanken Students
  • Code: 26053
  • Target groups: PhD / rMSc
  • Credit points: 5
  • Credit transfer: apply for substitution in Sisu
University of Helsinki Students
  • Code: COM-R314

  • Target groups: PhD / rMSc

  • Credit points: 5

FDPE Students Students
  • Target groups: PhD

  • Credit points: please check your curriculum

  • Credit transfer: please apply for credit transfer according to your home university's procedures

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