Econometrics 1 (ECOM-G314)

August 12, 2019

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Method of completion: contact teaching

Schedule:

  • Courses page (you can view the information on this site without logging in or registering, but some of the content added by teachers to course pages may be available to course participants only, for example Moodle course enrolment key) and/or
  • Sisu page

 

Study material:

  • Can be found in the Moodle learning platform
  • A link and a Moodle course key will be sent by email before the course starts and/or they will be provided on the Courses page, see above
  • Log in with your UH username to be able to use all the features of the course workspace

 

Enrollment:

  • In UH’s Sisu with your UH username
  • To be able to register for the course in Sisu, please note that
    • You must have a valid right to study at the course host university
    • You have created your primary personal study plan (HOPS) based on your study right
    • You have added the course for which you are registering to your HOPS
    • More information can be found on the webpage How to enroll in the courses?

 

Content:

The course builds upon a Bachelor-level introductory course in econometrics. A central goal is to deepen the knowledge on the linear regression model in various directions, including regression with instrumental variables and heteroskedastic errors. In addition, maximum likelihood estimation and the related asymptotic tests are introduced.

The course starts with a review of the linear regression model and the small-sample and asymptotic properties of the ordinary least squares estimator and statistical inference concerning its parameters. A large part of the course is devoted to the detection of and addressing violations of the basic assumptions of the linear regression model. In particular, statistical inference based on the ordinary least squares estimator under heteroskedastic or autocorrelated errors are considered. The instrumental variables and the generalised method of moments estimators, useful in the case of endogenous regressors as well as the method of maximum likelihood, widely applicable in econometrics, also introduced. Throughout the course, the emphasis is on the practical aspects of econometric modelling instead of the foundations of statistical inference. The models and methods are illustrated by means of Monte Carlo simulations and empirical applications.