Course description: The course covers key concepts in econometrics
and intends to widen and deepen students' knowledge of econometric
methods. The rst few lectures introduce some important econometric
estimators, focusing on asymptotic theory in a cross-sectional data
setting. The second part of the course is devoted to time series econometrics.
Techniques for modeling stationary and non-stationary time
series are discussed.
The main goal of this course is to provide a
fairly high level introduction to standard econometric theory. After
completing the course, students should be able to (i) understand the
theory behind the most commonly used econometric estimators; (ii)
critically evaluate and undertake empirical work in economics; (iii) undertake
more advanced studies in econometrics either in a classroom
setting or as self-study.
(i) For grade: four assignments (15% each) and a nal
exam (40%). (ii) For audit: students should turn in each assignment
and attain at least 50% on each; the nal exam is not required.
Mandatory for doctoral students unless exempted based
on evidence of previous studies. Optional for MA students; rst year
core econometrics sequence or instructor's consent is required. In particular,
students are assumed to be familiar with the classical linear
regression model and its basic assumptions, the OLS estimator, the
basics of hypothesis testing, and the most common tests in the classical
linear regression model (t, F).