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
WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. 7th ed. [S.l.] : South-Western/Cengage Learning, 2019. ISBN 978-1-3375-5886-0.
GUJARATI, Damodar N. Essentials of econometrics. 5th ed. New York : SAGE, 2023, 5th ed.. ISBN 9781071850398.
LUKÁČIKOVÁ, Adriana - LUKÁČIK, Martin - SZOMOLÁNYI, Karol. Úvod do ekonometrie s programom Gretl. Recenzenti: Veronika Miťková, Marian Reiff. 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.
VERBEEK, M. Panel Methods for Finance: A Guide to Panel Data Econometrics for Financial Applications. Berlin : De Gruyter, 2021. ISBN 978-3-11-066013-5
Články
Deev, O., Lyócsa, Š., & Výrost, T. (2022). The looming crisis in the Chinese stock market? Left-tail exposure analysis of Chinese stocks to Evergrande. Finance Research Letters, 49, 103154.
Lyócsa, Š., Baumöhl, E., & Výrost, T. (2022). YOLO trading: Riding with the herd during the GameStop episode. Finance Research Letters, 46, 102359.
Baumöhl, E., Bouri, E., Shahzad, S. J. H., & Výrost, T. (2022). Measuring systemic risk in the global banking sector: A cross-quantilogram network approach. Economic Modelling, 109, 105775.
Khalfaoui, R., Baumöhl, E., Sarwar, S., & Výrost, T. (2021). Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks. Resources Policy, 74, 102318.
Lyócsa, Š., Molnár, P., & Výrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data?. International Journal of Forecasting, 37(3), 1092-1110.
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: 28.11.2024