**Annotation**

This is an introductory course to econometrics with emphasis on its applications. During the lectures and practical exercises the main focus will be on helping students to learn modern methods of empirical analysis and their practical application using an appropriate software (mainly GRETL) to the real world data sets. The main topics cover regression analysis including an introduction to panel data regression (fixed effects model), binary response models (linear probability, logit, and probit models), introduction to time series, and simultaneous equations. Students are taught how to build a suitable econometric model, understand the strengths and limitations of empirical methods, correctly interpret results and draw valid conclusions.

**Aim of the course**

The aim of the course is to introduce main empirical methods of economic data analysis and to provide their theoretical foundations.

**Learning outcomes**

- Understand and apply fundamental econometric methods, basic concepts of data analysis: descriptive statistics, hypothesis testing, confidence intervals.
- Analyze and evaluate linear regression model: main assumptions, features and applications, develop abilities to choose suitable analytical tools.
- Understand the concept of non-linear analysis: main assumptions and features and be able to use suitable software, interpret regression results, build econometric models.
- Understand and apply linear probability model, logit model, probit model and its applications, be able to interpret regression results, build econometric models.
- Understand and apply time series regression: main models, basic assumptions of modeling and their violations, be able to interpret regression results, build econometric models.
- Understand the concept of panel data analysis, be able to interpret regression results, build econometric models.
- Understand and apply simultaneous equations: main assumptions, features, use data analysis results to make and to found sound economic or managerial decisions.