Information
Lectures:               Tuesdays 8am, room #314
Practice Sessions:  Thursdays 11am, room #016

Office hours:      Wednesdays 3:30-5pm
                 room #408

Course description
During this course we will go through the essentials of econometrics:
from the statistical background through the theory
and intuition behind the least squares estimation to practical applications.
Every topic will be backed up with an applied exercise.

Home assignments
There will be 4 homeworks assigned to the students during the course.
Performance in homeworks will constitute 40% of the final grade from the subject.
(The other 60% will be the final exam.)

Statistical software
1. Gretl - downloadable from http://gretl.sourceforge.net/
  Gretl User Guide
2. Stata - licenced software (installed in IES computer lab)
3. R-project downloadable from http://www.r-project.org/
  R User Guide

Syllabus
This is a tentative schedule to be updated basing on students' progress.
0. Introduction
    Lecture 1 (slides)
1. Repetition of mathematical and statistical background
- probability theory
- statistical inference
    Statistical inference (handout)
- matrix algebra
    Matrix algebra (handout)
2. Introduction to linear regression model
- derivation and interpretation of OLS
- assumptions in OLS regression models
- properties of OLS estimators
    Lecture 2 (slides)
    Lecture 3 (slides)
3. Testing hypotheses about regression parameters
    Lecture 4 (slides)
4. Multiple regression, nonlinear and dummy variables
    Lecture 5 (slides)
    Lecture 6 (slides)
5. Problems with OLS estimation in cross-sectional data
- heteroskedasticity
    Lecture 7 (slides)
- autocorrelation
- omitted variable
- measurement error
- simultaneity bias
6. Applying econometrics in practice
- 'reading' the data
- specification of the econometric equation
- interpretation of results