JEB109 - Econometrics I

Credit: 6
Credit ETCS: 6
Hours weekly: 2/2
Status: Bachelors - All
Bachelors - core
BEF - core
English
Semester - summer
Obligatory courses: JEB105 - Statistics
Recommended courses: JEB005 - Mathematics I
JEB006 - Mathematics II
JEB028 - Mathematics III
Course supervisors: prof. RNDr. Jan Ámos Víšek CSc.
Teachers: PhDr. Ladislav Krištoufek
Assistants: PhDr. Ladislav Krištoufek
PhDr. Boril Šopov, MSc., LL.M.
Schedule: Lectures: Tuesday, 9.30 - 10.50 a.m. Consultation hours: Tuesday, 12.30 a.m. - 2 p.m.
Announcements: The optional homework (given on lectures) should be sent as attached pdf files prepared preferably in TEX with the name in form

FamilyNameFirstName_HWnumber

on the address visek@fsv.cuni.cz
Literature: BASIC LEVEL:
Jeffrey M. Wooldridge (2006): Introductory Econometrics. A Modern Approach.
MIT Press, Cambridge, Massachusetts, second edition 2009

ALTERNATIVE A BIT ADVANCED:
Greene, W. H. (1993): Econometric Analysis. Macmillam Press, New
York.
Baltagi, B. H. (1999): Econometrics. Springer, Berlin.
Jeffrey M. Wooldridge, J. M. (2001):
Econometric Analysis of Cross Section and Panel Data. MIT Press,
Cambridge, Massachusetts, second edition 2008.
Judge, G. G., W. E. Griffiths, R. C. Hill, H. T.C. Lee (1982):
Introduction to the Theory and Practice of Econometrics. New York:
J.Wiley & Sons. (any other book, our library or library of CERGE)

ENGLISH ADVANCED:
http://samba.fsv.cuni.cz/~visek/Econometrics_Up_To_2010/

CZECH ALTERNATIVES - ADVANCED:
Anděl, J. (1978): Matematická statistika, SNTL & ALFA, Praha,
Bratislava.
Anděl, J. (1993): Statistické metody, MATFYZPRESS, Praha, 1993.
Víšek, J. Á (1997): Ekonometrie I, Nakladatelství Univerzity Karlovy v
Praze, 1997.
Víšek, J. Á (1997): Statistická analýza dat, Vydavatelství Českého vysokého učení technického v Praze, 1997.
Zvára, K. (1989): Regresní analýza, Academia, Praha.
(Any other book, I offer consultations on it or them.)
Description: The aim of the course is to provide possibility to learn basic theory of regression analysis, to process data by standard packages and to interpret results. The content of lectures of courses covers more or less the extent of the Part I of the book by Jeffrey M. Wooldridge (2006): Introductory Econometrics. A Modern Approach.
MIT Press, Cambridge, Massachusetts, second edition 2009.
It means that the following topics are successively discussed:
The linear regression model, assumptions, estimation - methods (moment method, the least squares, maximum likelihood), properties (unbiasedness, consistency, efficiency), frameworks (homoscedasticity, heteroscedasticity, collinearity, contamination), interpretation of results - quality of estimated model (significance, determination, normality, etc.), restrictions (linearity of model versus linearity of estimation), the "cloven hoofs" (ceteris paribus, coefficent of determination, etc.).
There is possibility to learn above the basic courses from lectures on the web - see literature above.
Content: For handouts (containing compressed but full content of lectures) see
http://ies.fsv.cuni.cz/en/staff/visek
DOWNLOADABLE

1. LECTURE
Motivation - what skills and competencies we should learn
together
The Nature of Econometrics and Economic Data
What is econometrics?
Steps in empirical economic analysis
The Structure of Economic Data
The Simple Regression Model with Cross-Sectional Data
Repetition of Simple Regression Model - from the
Statistics

2. LECTURE
The Regression Model with Cross-Sectional Data
Preliminaries for deriving the Estimators
Estimating by the Method of Moments
Estimating by the Ordinary Least Squares

3. Lecture
The Multiple Regression Model - continued
The assumptions and their consequences -
unbiasedness and existence of covariance matrix
Determination of the model
The ordinary least squares estimator

4. LECTURE
The Multiple Regression Model - continued
Misspecification of the linear model
Estimation of the variance of disturbances

5. LECTURE
The Multiple Regression Model - OLS Asymptotics
Efficiency the OLS-estimators
Sampling distribution of OLS-estimators
Significance of an explanatory variable and confidence
interval

6. LECTURE
The Multiple Regression Model - Asymptotics and Further
Issues
Significance of several explanatory vars and confidence
region
Effects of data scaling on OLS statistics
Reporting the results of regression analysis
Consistency

7. LECTURE
The Multiple Regression Model - continued
Asymptotic normality and large sample inference
Asymptotic efficiency of OLS
More on functional form

8. LECTURE
The Multiple Regression Model - Further Issues
Selection of (structure of) model
Confidence intervals for predictions

9. LECTURE
The Multiple Regression Analysis with Qualitative
Information
Binary and Dummy Variables
Combining qualitative and quantitative information
Qualitative response variable
Discussion of policy analysis

10. LECTURE
Heteroscedasticity
Consequences of heteroscedasticity for OLS
Heteroscedasticity-robust inference
Tests for heteroscedasticity
Weighted Least Squares Estimation

11. LECTURE
Specification of model
Functional form misspecification
Proxy variables for unobservable explanatory variables
Data Issues
Properties of OLS under measurement error

12. LECTURE
Missing Data and Nonrandom Sample
Missing data
Discussing “nonstandard” situations
Outliers and leverage points
Alternative methods to OLS

13. LECTURE (if all weeks are complete)
Robust data analysis - where are we nowadays?
A bit of history - motivation
Frustrations and rebirths
The least weighted squares - (LWS)
LWS - definition, properties, algorithm
LWS - how does it work ?
Problems with orthogonality condition
Instrumental variables - recalling definition
Robustifying instrumental variables - definition,
asymptotics and numerical study
Robust estimation of the model with effects
Establishing the theory
Numerical study
Seminar: There are 4 seminar groups:
Tuesdays 11:00 - 12:20 & 12:30 - 13:50, room 016 (L. Kristoufek) seminar handouts
Tuesdays 14:00 - 15:20 & 15:30 - 16:50, room 016 (B. Sopov)
Examination dates: Midterm test takes place on 5.4.2013 from 15:30 (in rooms 109 and 314).

Final tests will take place after the end of term and the day and hour will be discussed on one of last lectures (to fit with other exams).
Course requirements: To pass seminars, deliver homeworks and pass the final test with sufficient grades - see the handout of the first lecture on http://ies.fsv.cuni.cz/en/staff/visek.