Quantitative Methods in Empirical Research

Teachers

Included in study programs

Teaching results

In particular, students acquire the following abilities:
- principles for the formulation and specification of different types of econometric models,
- overview of the main methods of analysis of cross-sectional data, time series and models with discrete dependent variable,
- approaches to the specification, estimation and diagnosis of these econometric models.
Students acquire in particular the following skills:
- ability to apply research methods to specific problems using suitable software.
Students will acquire the following competencies:
- the ability to correctly select the appropriate econometric method for a research problem,
- identify potential violations of model assumptions and propose a solution to handle them,
- correctly interpret the results of econometric models.

Indicative content

• Estimation, asymptotic properties and diagnostics of regression models.
• Autocorrelation – identification, testing and solutions.
• Heteroskedasticity – identification, testing and solutions.
• Qualitative regressors, model specification and testing.
• Time series – basic model specification, Box-Jenkins methodology, ARIMA.
• Stationarity of time series and its testing, stationarization of time series.
• Cointegration a Granger causality.
• Models with a discrete dependent variable.

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
Lyócsa, Š., Plíhal, T., & Výrost, T. (2021). FX market volatility modelling: Can we use low-frequency data?. Finance research letters, 40, 101776.
Lyócsa, Š., Todorova, N., & Výrost, T. (2021). Predicting risk in energy markets: low-frequency data still matter. Applied Energy, 282, 116146.
Lyócsa, Š., Výrost, T., & Plíhal, T. (2021). A tale of tails: New evidence on the growth-return nexus. Finance Research Letters, 38, 101526.
Lyócsa, Š., Baumöhl, E., Výrost, T., & Molnár, P. (2020). Fear of the coronavirus and the stock markets. Finance research letters, 36, 101735.

Syllabus

• Regression repetitorium, model assumptions, asymptotic properties and diagnostics. • The most common problems of econometric models – autocorrelation, consequences and solutions. • The most common problems of econometric models – heteroskedasticity, consequences and solutions. Method of weighted least squares, robust estimates. • Qualitative regressors, model specification and testing. • Time series, trends and seasonality. ARIMA models, specification and estimation. • Time series stationarity, testing of unit roots in time series and panel data, problem of spurious regression. • Cointegration and error-correction models. Granger causality. • Models with discrete dependent variable.

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: 06.03.2024

Date of the latest change: 28.11.2024