Applied Research Methods
- Credits: 10
- Ending: Examination
- Range: 16sP
- Semester: summer
- Year: 1
- Faculty of Commerce
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
HATRÁK, Michal. Ekonometria. Bratislava. IURA EDITION, 2007. 503 s. Ekonómia. ISBN 978-80-8078-150-7.
WOOLDRIDGE, Jeffrey M. Introduction to econometrics. Europe, Middle East and Africa edition. Hampshire : Cengage Learning, 2014. 603 s. ISBN 978-1-4080-9375-7.
WOOLDRIDGE, Jeffrey M. Econometric analysis of cross section and panel data. 2nd ed. Cambridge : The MIT Press, 2010. xxvii, 1064 s. ISBN 978-0-262-23258-6.
GUJARATI, Damodar N. - PORTER, Dawn C. Basic econometrics. 5th international ed. New York : McGraw-Hill/Irvin, 2008, 5th ed., 2009. xx, 922 s. ISBN 9780073375779.
LUKÁČIKOVÁ, Adriana - LUKÁČIK, Martin - SZOMOLÁNYI, Karol. Úvod do ekonometrie s programom Gretl. 1. vydanie. Bratislava : Letra Edu, 2018. 345 s. ISBN 978-80-972866-5-1.
LUKÁČIKOVÁ, Adriana - LUKÁČIK, Martin - SZOMOLÁNYI, Karol. Úvod do ekonometrie s programom Gretl. 1. vydanie. Bratislava : Letra Edu, 2018. 345 s. ISBN 978-80-972866-5-1.
VÝROST, Tomáš - BAUMÖHL, Eduard - LYÓCSA, Štefan. Kvantitatívne metódy v ekonómii III. 1. vyd. Košice : ELFA, 2013. 391 s. ISBN 978-80-8086-211-4.
Články
LYÓCSA, Štefan - VÝROST, Tomáš. To Bet or Not to Bet: A Reality Check for Tennis Betting Market Efficiency. In Applied Economics. - London : Taylor & Francis. 2018, vol. 50, no. 20, pp. 2251-2272. ISSN 1466-4283.
LYÓCSA, Štefan - VÝROST, Tomáš - BAUMÖHL, Eduard. Return Spillovers Around the Globe: A Network Approach. In Economic Modelling. - Amsterdam : Elsevier Science. 2019, vol. 77, pp. 133-146. ISSN 0264-9993.
LYÓCSA, Štefan - BAUMÖHL, Eduard - VÝROST, Tomáš - MOLNÁR, Peter. Fear of the Coronavirus and the Stock Markets. In Finance Research Letters. - New York : Elsevier, 2020, vol. 36, pp. 1-7. ISSN 1544-6123.
LYÓCSA, Štefan - MOLNÁR, P. - VÝROST, T. Stock market volatility forecasting: Do we need high-frequency data? In: International Journal of Forecasting. New York : Elsevier. 2021. ISSN 0169-2070.
VÝROST, Tomáš - LYÓCSA, Štefan - BAUMÖHL, Eduard. Network-Based Asset Allocation Strategies. In North American Journal of Economics and Finance. - Amsterdam : Elsevier Science B.V. 2019, vol. 47, january, pp. 516-536. ISSN 1879-0860.
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
Total study load (in hours): 10 credits x 26 hours = 260 hours
Distribution of study load
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: 06.03.2024
Date of the latest change: 16.01.2022