Introductory Econometrics

Home assignment

Problem sets will be assigned via MS Teams (See Files → Výukové materiály).

Exercises

Exercises for exercise classes.

Lecture presentations

• Lecture 1: Introduction
• Lecture 2: Simple regression I
• Lecture 3: Simple regression II
• Lecture 4: Multiple regression
• Lecture 5: Hypothesis testing
• Lecture 6: Heteroskedasticity
• Lecture 7: More on functional forms
• Lecture 8: Predictions, see also this R tutorial
• Lecture 9: Gentle introduction to regression with time series
• Lecture 10: More on random processes and serial correlation

Data files

• Excel and csv (with some pdf annotations): advertising.csv, attend.csv, attend.pdf, automobile_market.csv, ceo.xls, fertil3.csv, fertil3_desc.csv, farms.csv, farms2.csv, food.xlsx, GEM.csv, GEM_desc.csv, housing_market.csv, labour_market.csv, MonteCarlo.xls, MonteCarlo2.xls, MonteCarlo3.xls, okun.csv, okun2.csv, simplereg.xls, skoda.csv, sleep75.csv, sleep75_desc.csv, used_cars.xls, used_cars_original.xls, wage1.csv, wage1_desc.csv
• R: attend.RData, wage1.RData, wage2.RData, sleep75.RData, bwght.RData, ceosal1.RData,
• Gretl: attend.gdt, automobile_market.gdt, barium.gdt, bweight.gdt, ceosal1.gdt, creditcards.gdt, durgoods.gdt, farms.gdt, farms2.gdt, fertil.gdt, GEM.gdt, GPA.gdt, houses.gdt, housesPS2.gdt, housing_market.gdt, labour_market.gdt, lunch.gdt, okun.gdt, phillips.gdt, salmon.gdt, sleep.gdt, sleep_complete.gdt, skoda.gdt, skoda2.gdt, voting.gdt, wage.gdt, wage2.gdt, wage3.gdt, wine.gdt

Other materials

• info about the STAR experiment
• demonstration of CLT (Central limit theorem), a Matlab script
• an example of descriptive, causal and forecasting interpretation
• solutions to selected exercises from tutorial 1

Course requirements

• 20 points: home assignments (2 problem sets, 10 points each).
• 30 points: a series of 5 minitests (tentative dates: week 4, 6, 8, 10 and 12 of the semester); each test will contain 2 questions from this list. The range of questions for each minitest will be announced via email in advance.
• 50 points: final test (last lecture + three more dates in the exam period).

Grading scale

• 90 – 100 points: excellent (1)
• 75 – 89 points: very good (2)
• 60 – 74 points: good (3)
• 0 – 59 points: failed (4)

Recommended reading

• Lecture notes and presentations.
• Wooldridge, J. M.: Introductory econometrics: A modern approach, 4e. Mason: South-Western, 2013.
• Gujarati, D. N.: Basic econometrics, Boston: McGraw-Hill, 2004.
• Virtually any other book on econometrics. Note: for Czech students, several Czech alternatives are given in the course syllabus.

Gretl

• Gretl homepage
• an extensive set of data files from popular econometrics textbooks

R

• Step 1: Download R (Windows version)
• Step 2: Download RStudio, the best graphical user interface (and much more than that) for R