Econometrics I - JEB109

• Anglický název: Econometrics I
• Zajišťuje: Institut ekonomických studií (23-IES)
• Fakulta: Fakulta sociálních věd
• Platnost: od 2016
• Semestr: letní
• Body: 6
• E-Kredity: 6
• Způsob provedení zkoušky: letní s.:
• Rozsah, examinace: letní s.:2/2 Zk [hodiny/týden]
• Počet míst: 120 / 120 (120)
• Minimální obsazenost: neomezen
• Stav předmětu: vyučován
• Jazyk výuky: angličtina
• Způsob výuky: prezenční
• Poznámka: předmět je možno zapsat mimo plán; povolen pro zápis po webu; při zápisu přednost, je-li ve stud. plánu
• Garant: doc. PhDr. Ladislav Krištoufek, Ph.D.
• Vyučující: Periklis Brakatsoulas; Olena Chorna, M.A.; doc. PhDr. Ladislav Krištoufek, Ph.D.; Petr Pleticha, M.Sc.; Jan Šíla, M.Sc.
• Prerekvizity : JEB105
• Je prerekvizitou pro: JEB110

Anotace

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (29.01.2015)

Introductory course of econometrics focusing on cross-sectional data analysis within the ordinary least squares framework.

Cíl předmětu

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (29.01.2015)

The main aim of the course is to train students to be able to properly analyze a cross-sectional dataset using the OLS framework, construct models, and interpret the results as well as to prepare them for further Econometrics courses.

Literatura

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (29.01.2015)

CORE TEXT:
Jeffrey M. Wooldridge (2012): Introductory Econometrics. A Modern Approach.
CENGAGE Learning Custom Publishing, 5th Edition

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)

Metody výuky

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (22.02.2019)

Lectures: Tuesdays, 9:30 - 10:50
Office (consultation) hours: Tuesdays, 11:00 - 12:00 (Office 406)

 

There are 4 seminar groups:

• S1: Tuesdays 12:30 - 13:50, room 016 (O. Chorna)
• S2: Tuesdays 14:00 - 15:20, room 016 (P. Pleticha)
• S3: Tuesdays 15:30 - 16:50, room 016 (J. Sila)
• S4: Wednesdays 15:30 - 16:50, room 016 (P. Brakatsoulas)

Seminars software: R/R Studio (available on all 016 computers)

Požadavky ke zkoušce

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (22.02.2019)

The final grade consists of three ingredients:

• Midterm: 25
• Home assignments: 15 (2*7.5)
• Final exam: 60

Grading scale:

• A: above 90 (not inclusive)
• B: between 80 (not inclusive) and 90 (inclusive)
• C: between 70 (not inclusive) and 80 (inclusive)
• D: between 60 (not inclusive) and 70 (inclusive)
• E: between 50 (not inclusive) and 60 (inclusive)
• F: below 50 (inclusive)

 

Midterm exam:

• 2.4.2019, 9:30AM-10:50AM, room 109 (lecture time and place)

Final exam:

• Pre-term: 21.5.2019, 9:30AM, room 109
• Term 1: 28.5.2019, 9:30AM, room 109
• Term 2: 6.6.2019, 9:30AM, room 109
• Term 3: 18.6.2019, 9:30AM, room 109
• Resit term: 3.9.2019, 9:30AM, room 109

 

Sylabus

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (22.02.2019)

WEEK #1:

• Course information
• Statistics review

WEEKS #2 - #4:

• Steps Empirical Economic Analysis
• Structure of Economic Data
• Causality and Ceteris Paribus Notion
• Simple regression analysis
• Multiple Regression
• Derivation of the Ordinary Least Squares (OLS) Estimates
• Properties of OLS
• Units of Measurement and Functional Form
• Expected Values and Variances of the OLS Estimators
• Interpretation of OLS
• Efficiency of OLS: The Gauss-Markov Theorem

WEEKS #5 - #6:

• Sampling Distribution of the OLS Estimators
• Testing Hypotheses: The <i>t</i> Test
• Confidence Intervals
• Testing Multiple Linear Restrictions: The <i>F</i> Test
• Reporting Regression Results
• Consistency
• Asymptotic Normality and Large Sample Inference
• Asymptotic Efficiency of OLS

WEEK #7:

• MIDTERM EXAM

WEEKS #8 - #10:

• Effects of Data Scaling on OLS Statistics
• More on Functional Form
• Goodness-of-Fit and Selection of Regressors
• Prediction and Residual Analysis
• A Single Dummy Independent Variable
• Using Dummy Variables for Multiple Categories
• Interactions Involving Dummy Variables
• A Binary Dependent Variable: The Linear Probability Model
• Interpreting Regression Results with Discrete Dependent Variables

WEEKS #11 - #12:

• Consequences of Heteroskedasticity for OLS
• Heteroskedasticity-Robust Inference after OLS Estimation
• Testing for Heteroskedasticity
• Weighted Least Squares (WLS) Estimation
• Functional Form Misspecification
• Using Proxy Variables for Unobserved Explanatory Variables
• Models with Random Slopes
• Properties of OLS under Measurement Error
• Missing Data, Nonrandom Samples, and Outlying Observations

WEEKS #13:

• Revision

Vstupní požadavky

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (29.01.2015)

Obligatory: Statistics I

Suggested: Mathematics I + II + III

Požadavky k zápisu

Poslední úprava: doc. PhDr. Ladislav Krištoufek, Ph.D. (29.01.2015)

There are no restrictions.