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The main focus of this course is to develop computational skills helpful in utilising both microeconomic and macroeconomic data. Throughout this course selected econometric techniques will be presented: multiple regression, binary choice models, panel models, count data models, and time series procedures.

Mathematics, statistics, mathematical statistics, microeconomics, macroeconomics.

Maddala, G. S. (1977), Econometrics, McGraw-Hill, New York.

Chow, G. C. (1983) Econometrics, McGraw-Hill, New York.

Research Project (40%):

You are going to prepare, present, and defend a written research project based on the procedures presented throughout the course. To improve your team work ability the projects as well as the homework assignments could be prepared in groups (max. of 4 persons).

Homework Assignments (15%):

Homework assignments should be submitted as an e-mail attachment (preferably in .pdf, .pptx or .docx format). The deadline for submition expires every Wednesday 10 p.m. prior to the next class. Homework assignment should be prepared in groups (see Research Project).

Midterm Examinations (20%):

1. Week 4 – Linear regression;
1. Week 8 – Binary and ordinal choice models;
1. Week 13 – Count data models; introduction to time series modelling (unit root, stability, Granger causality)

Final Examination (25%):

There will be a writen examination at the end of the course.

Lecture and Laboratories:

A (5) 85-100 %
B (4+) 80-84 %
C (4) 70-79 %
D (3+) 60-69 %
E (3) 51-59 %
F (2) below 50 %
(Polish equivalent in parentheses)

Block 1: Multiple Regression

Activity: marginal effects; diagnostics: normality of residuals, heteroscedasticity, expected value of residuals; goodness-of-fit: R^2, adjusted R^2, log-likelihood; information criteria; forecasting

R Script: Estimation, significance, and goodness-of-fit in R
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Presentation: Plotly graphs for OLS estimations
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Data and Exercises: The curious case of used Audi Q5
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R Script: Basic operations in R
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R Script: Multidimensional graphs in R Plotly
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Hints: Interpreting OLS results
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Block 2: Binary and Ordinal Choice Models

Activity: binary choice variables; ordinal choice variables; logit and probit; marginal effects; diagnostics; goodness-of-fit: pseudo-R^2; forecasting probability: conditional probability response

Lecture: Non-linear procedures; probit; logit
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Lecture: Ordinal logit
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Data: Price change probability
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