wallusch@ikbt.org

The aims of this lecture are as follows: [1] To present selected methods of quantitative data analysis for business and economics; [2] To provide students with decision-making capabilities based on the quantitative analyses; [3] To improve students’ use of econometric and statistical packages;

Mathematics, statistics, microeconomics, macroeconomics.

Some Helpful Files: course outline, description of homework assignments, grading scale matrix
download files in .rar-format

Kabacoff, R. I. (2011), R in Action. Data Analysis and Graphics with R, Manning

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

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

Charemza, W. W. and Deadman, D. F. (1992), New Directions in Econometric Practice, Edward Elgar

GRETL:
download for Windows useres
download for Mac useres

Research Project (60%):

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 (5%):

Homework assignments should be submitted as an e-mail attachment (preferably in .pdf or .docx format) prior to the next class.

Midterm Examinations (10%):

1. Week 5 – Multiple regression, OLS;
2. Week 8 – Conditional probability, binary choice models (logit, probit);
3. Week 11 – Panel data models, count data models;
3. Week 14 – Time series (Granger causality, impulse-response, variance decomposition);

Final Examination (25%):

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

Lecture and Laboratories:

A (6) 90-100 %
B (5) 80-89 %
C (4) 70-79 %
D (3+) 60-69 %
E (3) 51-59 %
F (2) below 50 %
(Polish equivalent in parentheses)

Week 1.: Introduction

Activity: data preparation, helpful tricks in Excel; filtering;

Data and Exercises: Forex; regional unemployment; OLS;

Week 2.: Introduction to R

Activity: importing data to R; R packages; basic operations; time series and dates

R Script: descriptive stats, zoo-object, Plotly graph

Lecture: introduction to R;

Data and Exercises: Foreign exchange rate: USD/PLN and EUR/PLN;

Week 2.: Clustering. Hypothesis Testing. Introduction to OLS

Activity: probability distributions; histogram and kernel density; testing (Kolmogorov-Smirnof test, equality of two means, two variances, two proportions); ANOVA; using the Central Limit Theorem; estimation - OLS

R Script: k-Means clustering

Data and Exercises: hypothesis testing;

Data and Exercises: Redskins' Payroll 2015: hypothesis testing;

R Script: histogram, kernel distribution, and hypothesis testing

Examples: testing: examples;

Data and Exercises: marginal effects, slope;

Data and Exercises: OLS in GRETL;

Lecture: linear models in R (part 1);

R Script: estimation, significance, and goodness-of-fit in R

Presentation: Plotly graphs for OLS estimations;

Data and Exercises: The curious case of used Audi Q5;

Week 3.: 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

Exercises: Dr. Plama and his car;

Exercises: OLS and elasticity;

Data for Exercises: Dr. Plama and his car;

Week 4.-5.: Binary Choice Models. Survey Data

Activity: (week 1) marginal effects; diagnostics: normality of residuals, heteroscedasticity, expected value of residuals; goodness-of-fit: deviance, pseudo-R^2 and pseudo-adjusted R^2, log-likelihood; information criteria

Lecture: non-linear procedures; probit; logit;

Data and Exercises: calculating the marginal effects;

Data and Exercises: Young Doctors;

R Script: contingency and frequency tables in R

R Script: Probit/Logit in R: estimation and goodness-of-fit

R Script: Probit/Logit in R: margial effects

Week 6.: Ordinal Choice Models

Activity: marginal effects; conditional probability; diagnostics; goodness-of-fit

Lecture: ordinal logit;

Data: price change probability;

R Script: ordinal logit in R

Week 7.: Count Data Models.

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

Data: doctor visits;

Lecture: PowerPoint presentation;

R Script: PowerPoint presentation;

Week 8.: Introduction to Clustering.

Activity: clustering; distance; k-means; mean shift

Lecture: PowerPoint presentation;

R Script: k-Means;

R Script: Mean Shift;

Week 8.-9.: Panel Data Models.

Activity: pooled models; fixed and random effect models; marginal effects; diagnostics: normality of residuals, heteroscedasticity, expected value of residuals; goodness-of-fit: R^2, adjusted R^2, log-likelihood; information criteria

Lecture: PowerPoint presentation;

Week 10.-12.: Time Series Models. Part I

Activity: stationary and non-stationary time series; testing for unit root; spurious regression; detrending;

Exercises: Spurious correlation and trends;

R Script: Unit root and decomposition;

Data: Highly seasonal data on tourism in Poland;

Lecture: PowerPoint presentation;

Week 10.-12.: Time Series Models. Part II

Activity: Granger causality; Vector Autoregression (VAR); model selection; stability; impulse-response analysis; variance decomosition

Exercises: Exercises;

Lecture: PowerPoint presentation;

Week 15.: Data and Result Visualisation in R

R Script: multidimensional graphs in R Plotly