Econometrics 1. Course Syllabus CEU, Fall 2009

Gabor Kezdi kezdig@ceu.hu

Instructor: Gabor Kezdi (kezdig@ceu.hu)

Teaching Assistant: Agnes Szabo-Morvai (szabo_agnes@ceu-budapest.edu)

Course prerequisite: Mathematical Statistics (CEU MA 1^st)

Credits: 3 CEU credits (6 ECTS credits)

Course website: http://www.personal.ceu.hu/staff/Gabor_Kezdi/Econometrics-1/econometrics-1.htm

Course website:

http://www.personal.ceu.hu/staff/Gabor_Kezdi/Econometrics-1/econometrics-1.htm

Main text:

Wooldridge, Jeffrey M., Introductory Econometrics, 2^nd ed. Thompson, 2003.

Goals. Econometrics 1 gives a thorough introduction of linear regression analysis, the workhorse of applied econometric analysis. The course covers the conceptual framework, the most important formal results and the practical question related to regression analysis. It also introduces an econometric software in order to carry out estimation and testing.

Learning outcomes. Successful completion of the course enables students to

Understand how linear regression is used to estimate causal relationships from observational data.

Derive solutions to structured and semi-structured problems related to the specification, estimation and testing of linear regression models.

Argue for and against the use of specific control variables in linear regression models.

Prove consistency or find asymptotic bias of linear estimators.

Understand the logic of sampling variance and distribution of estimators.

Carry out simple hypothesis tests in linear models.

Estimate the models covered in the course using econometric software, and interpret their results.

Course outline

Week 1	Introduction: causal effects and data structures. Simple regression. Introducing ourselves. Chapters 1 & 2.
Week 2	Simple regression, cont. Multiple regression: Estimation. Computer session: Introduction to EViews. Chapters 2 & 3.
Week 3	Multiple regression: Inference. OLS asymptotics. Chapters 4 & 5.
Week 4	Multiple regression: further issues Chapter 6.
Week 5	Dummy variables. Heteroskedasticity. Chapters 7 & 8.
Week 6	Summary and review

Grading

25% from problem sets

75% from final exam

Passing the course requires scoring 50% or higher on the final exam