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LEARNING OBJECTIVES:
After successful completing the course, the participants can
COURSE DESCRIPTION:
The course focuses on derivation of asymptotic theory for extremum estimators and tests. The class of extremum estimators includes maximum likelihood, GMM, non-linear least squares estimators, simulated ML, simulated MM and indirect inference (EMM) estimators. To derive the asymptotic theory, a foundation in probability theory is provided. In addition, the precision of the asymptotic theory is evaluated by Monte Carlo techniques.
COURSE SUBJECT AREAS:
The main part of the course covers asymptotic theory. Different applications will be considered.
Asymptotic theory
Possible applications of the asymptotic theory
PREREQUISITES: 4630: Microeconometrics or 4616: Time series econometrics.
LECTURER: Henning Bunzel, Robinson Kruse and Allan Würtz
TEACHING METHOD: 4 lectures weekly for 10 weeks. There will be homework to practice the various inference methods including computer exercises.
LITERATURE:
The asymptotic theory is covered by the following:
Newey and McFadden: "Large sample estimation and hypothesis testing". In Handbook of Econometrics vol. IV, 1994.
White: Asymptotic Theory for econometricians. Academic Press, 1999. Chapters 2, 3, 4, 5.
A selection of articles relevant for the choices of applications of the asymptotic theory.
FORM OF ASSESSMENT: The assessment is based on points collected from home-work assignments and from the take-home exam.
EXAMINATION AIDS ALLOWED: All - except any means of electronic communication including calculators, mobile phones and PC's.