# Applied Research Methods

- Credits: 10
- Ending: Examination
- Range: 16sP
- Semester: summer
- Year: 1
- Faculty of Economic Informatics

## Teachers

## Included in study programs

**Teaching results**

In particular, students acquire the following abilities:

- principles for the formulation of an empirical research project, in particular in relation to the nature of the variables entering the analysis as well as research sample,

- an overview of the main methods of descriptive statistical analysis and inferential statistics,

- specification and estimation of econometric models (linear, panel and quantile regression).

Students acquire in particular the following skills:

- ability to apply research methods to specific problems using suitable software.

Students will acquire the following competencies:

- ability to define a research problem, to select a research sample adequately, and to choose an appropriate method for answering a research question,

- distinguish between methods depending on the nature of the data used, both in the case of cross-sectional, panel data or time series,

- correctly interpret the output of econometric models and implement the necessary diagnostics.

**Indicative content**

• Basics of empirical applied research - research sample and statistical inference.

• Applied approaches for descriptive statistics and statistical inference, statistical software.

• Linear regression – model specification, estimation and applications.

• Linear regression problems – violation of assumptions and their resolution.

• Panel data and panel models – specification, estimation and inference.

• Dynamic panel models – specification, estimation and inference.

• Analysis of quantiles and quantile dependence.

• Economic time series modelling.

**Support literature**

WOOLDRIDGE, J. M. Introduction to econometrics : Europe, Middle East and Africa edition. Hampshire : Cengage Learning, 2014. 603 p. ISBN 978-1-4080-9375-7.

WOOLDRIDGE, J. M. Econometric analysis of cross section and panel data. 2nd ed. Cambridge : The MIT Press, 2010. xxvii, 1064 p. ISBN 978-0-262-23258-6.

GUJARATI, D. N. - PORTER, D. C. Basic econometrics. 5th international ed. New York : McGraw-Hill/Irvin, 2008, 5th ed., 2009. xx, 922 p. ISBN 9780073375779.

Research papers

LYÓCSA, Š.- VÝROST, T. To Bet or Not to Bet: A Reality Check for Tennis Betting Market Efficiency. In Applied Economics. - London : Taylor & Francis. ISSN 1466-4283, 2018, vol. 50, no. 20, pp. 2251-2272

LYÓCSA, Š. - VÝROST, T. - BAUMÖHL, E. Return Spillovers Around the Globe: A Network Approach. In Economic Modelling. - Amsterdam : Elsevier Science. ISSN 0264-9993, 2019, vol. 77, pp. 133-146

LYÓCSA, Š.- BAUMÖHL, E. - VÝROST, T. - MOLNÁR, P. Fear of the Coronavirus and the Stock Markets. In Finance Research Letters. - New York : Elsevier. ISSN 1544-6123, 2020, vol. 36, pp. [1-7]

LYÓCSA, Š. - MOLNÁR, P. - VÝROST, T. Stock market volatility forecasting: Do we need high-frequency data? In: International Journal of Forecasting. - New York : Elsevier. ISSN 0169-2070, 2021.

VÝROST, T.- LYÓCSA, Š. - BAUMÖHL, E. Network-Based Asset Allocation Strategies. In North American Journal of Economics and Finance. - Amsterdam : Elsevier Science B.V. ISSN 1879-0860, 2019, vol. 47, pp. 516-536.

**Syllabus**

• Key concepts in probability theory, descriptive and inferential statistics with cases for applied research. Research sample and implications for statistical inference. • Applications of descriptive statistics and statistical inference in using software support. • Linear regression analysis — estimation methods, interpretation, model suitability assessment, prediction and inference. • Linear regression analysis – testing assumptions and alternative solutions in case of their violations. • Panel regression models – LSDV approaches, fixed and random effects. Practical aspects of estimating panel models, diagnostics. • Dynamic panel models – estimation, testing and applications. • Quantile regression and modern approaches to quantile dependence modelling. • Selected applications and problems in time series modeling.

**Requirements to complete the course**

40% assignments (2 assignments x 20 points);

60% final paper

**Student workload**

Lectures and seminar participation: 16 hours

Preparation for seminars: 32 hours

Written assignments: 32 hours

Final paper preparation: 158 hours

Preparation of presentation and presentation itself: 20 hours

Consultation for final paper: 2 hours

**Language whose command is required to complete the course**

Slovak, English

Date of approval: 08.02.2023

Date of the latest change: 12.01.2022