Individual depending on the topic of the dissertation
A doctoral student may apply for the defense of their dissertation if:
- They have successfully passed the dissertation exam
- Has fulfilled the conditions for registering for the defense of the dissertation.
The defense of the dissertation is conducted as a scientific discussion involving the doctoral student, opponents, members of the committee, and other participants in the defense.
Slovak and English
- Introduction by the Chairperson: The chairperson introduces the doctoral candidate.
- Presentation by the Doctoral Candidate: The candidate presents the thesis content, key results, and its contributions.
- Statements by the Opponents: The opponents present their assessments, or the statements are read in full.
- Statement by the Doctoral Candidate: The candidate responds to the opponents' statements, addresses objections, and answers their questions.
- Discussion: Open discussion where the candidate answers questions and responds to suggestions raised during the discussion.
- Final Statements by the Opponents and Committee Evaluation
The graduate is able to work independently and creatively with scientific sources, to analyze and evaluate the current state of the problem to be solved in his/her field, to synthesize and apply theoretical knowledge. He/she is able to adequately select and effectively apply research methods, present and defend his/her professional opinion on problems and actively search for solutions. At the same time, he/she demonstrates the language and professional culture as well as his/her own attitude towards the professional issues of his/her studies.
Recommended literature is part of the doctoral student's study plan
The student submits a written project for the dissertation exam within the set deadline, which will be presented during the dissertation exam and then be the subject of a professional discussion. The state exam will also include an oral answer to two questions, which will be based on the content focus of the dissertation and on individually studied literature formulated from subjects completed within the study plan,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
English and Slovak
- Preparation and submission of a written project for the dissertation examination by the specified deadline.
- Presentation and defense of the dissertation project.
- Scientific discussion about the presented project.
- Answering two questions on the main content of the dissertation.
- The questions are based on the individual literature study and the topics
The dissertation examination serves to determine the doctoral candidate's readiness to continue their studies
As recommended by the supervisor
Interim assessment: evaluation of work with literature,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Research activity based on the recommendations of the supervisor.
• Work with scientific texts.
Upon completion of the course, the student has mastered working with information sources, is oriented in various databases, knows how to search, understands various citation indexes.
As suggested by the student, subject to the approval of the supervisor.
Interim assessment: evaluation of work with literature,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Research activity based on student's and supervisor’s suggestions.
• Work with scientific texts.
Upon completion of the course, the student has mastered working with information sources, is oriented in various databases, knows how to search, understands various citation indexes.
• Barro, R. J. & Gordon, D. B. 1985. A positive theory of monetary policy in a natural rate model, Journal of Political Economy, 91/4, 589-610
• Bernanke, B. S. & Mishkin, F. S. 1997. Inflation targeting: A new framework for monetary policy? Journal of Economic Perspectives, 11/2, 97-116
• Blanchard O. & Fischer S. 1989. Lectures on Macroeconomics. Cambridge, MA: MIT Press.
• Domonkos, T. & Ostrihoň, F. & Šikulová, I. & Širaňová M.: Analysing the Relevance of the MIP Scoreboard's Indicators. National Institute Economic Review, Vol 239, Issue 1, 2017
• Gáli, J. 2015. Monetary policy, inflation, and the business cycle, Princeton University Press, 2nd edition.
• Hindriks, M. 2006. Intermediate Public Economics. Cambridge: MIT Press. 2006.
• Fernandez-Villaverde J. & Fabio Rubio-Ramirez J. & Schorfheide F. 2016. Solution and Estimation Methods for DSGE Models. Handbook of Macroeconomics 2, pp. 527–724
• Kydland, F. E. & Prescott, E. C. 1977. Rules rather than discretion: The inconsistency of optimal plans, Journal of Political Economy, 85/3, 473-492
• Miťková V. & Mlynarovič V. 2010. Makroekonomická analýza. Wolters Kluwer (Iura Edition)
• Romer D. 2012. Advanced macroeconomics. 4th ed. McGrew - Hill
• Taylor, J. B. 1993. Discretion versus policy rules in practice, Carnegie-Rochester Conference Series on Public Policy, 39, December, 195-214
• Walsh, C. E. 2017. Monetary theory and policy, The MIT Press, 4th edition.
• Woodford, M. 2003. Interest and Prices: Foundations of a Theory of Monetary Policy, Princeton University Press.
50 points: semestral project
50 points: viva voice exam and/or written exam,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Structural models, RBC theory
• Nominal Rigidities, Dynamic Stochastic General Equilibrium Models
• Monetary Policy in Classical RBC Models, Monetary Policy in NK Models
• Monetary Policy Design (Rules vs. Discretion) and Central Bank Independence
• Fiscal rules and macroeconomic stability
• Public Sector Growth theories
• Governmental Debt Dynamics and Fiscal competition
Upon completion of the course, the student:
• Will be able to understand theoretical and practical aspects of DSGE models and fiscal as well as monetary policy theories
• Can understand and design dynamic macroeconomic models
• Will be able to use modern macroeconomic modelling tools
• Altman, M. Handbook of contemporary behavioral economics: foundations and developments. Routledge, 2015
• Akerlof, G.A. 1970. The market for ‘lemons’: Quality uncertainty and the market mechanism. Quarterly Journal of Economics 84: 488–500
• Banks, J.S., and J. Sobel. 1987. Equilibrium selection in signalling games. Econometrica 55: 647–661
• Bolton, P., & Dewatripont, M. (2005). Contract Theory. MIT Press.
• Fendek, M. Kvantitatívna mikroekonómia. Iura edition, (1999). 346 s., ISBN 80-88715-54-7
• Frank R.H.,(1996) Mikroekonomie a chování. Praha 1996
• Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic Theory. Oxford University Press.
• Jánošová, M., & Domonkos, T., & Ostrihoň, F.(2015) Poverty trends in Europe and Slovakia. In Inclusive growth and employment in Europe : peer-reviewed international conference proceedings, Bratislava 3rd-4th november 2015.
• THALER, Richard H.; BENARTZI, Shlomo. Save more tomorrow™: Using behavioral economics to increase employee saving. Journal of political Economy, 2004, 112.S1: S164-S187
• Tirole, J. (1988). The Theory of Industrial Organization. MIT Press.
• Varian, H. R. (1992). Microeconomic Analysis. W.W. Norton & Company.
50% evaluation for a semester project on a chosen topic
50% evaluation for a final oral exam,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Advanced Microeconomic Theory
Equilibrium and Efficiency in General Equilibrium: Theoretical Foundations and Applications
Analysis of Consumer and Production Decisions: Models and Their Implications
• Market Mechanisms and Resource Allocation
Resource Allocation Algorithms and the Design of Efficient Markets
Practical Applications: Auctions (types of auctions, their design, and efficiency) and Labor Markets
Models of Supply and Demand in the Labor Market
• Asymmetric Information
Hidden Information
Hidden Action in Market Structures
Signaling and Screening: Mechanisms for Resolving Information Asymmetry
Adverse Selection and Moral Hazard: Models and Their Economic Consequences
• Behavioral Economics
Dynamics Between Short-term and Long-term Preferences: Postponement and Impulsive Behavior, Tools for Conflict Resolution
Cognitive Constraints and Emotional Factors: Their Influence on Economic Decisions and Impact on Economic Behavior
Practical Applications of Behavioral Economics: Personal Finance, Public Policy, Corporate Strategies
• Economic Aspects of Development and Poverty
Microeconomic Causes of Poverty and Social Inequality
The Role of Institutions in Developing Economies and Their Impact on Growth
Study of Successful Development Policies (e.g., Employment Promotion Programs)
Upon completion of the course, the student will acquire advanced knowledge of microeconomic theory, equilibrium analysis and market mechanisms. He will be able to apply basic principles and advanced concepts of microeconomics to the analysis of economic decisions and their impact on welfare. They will acquire the ability to model real market situations and will be able to independently conduct and critically evaluate economic studies using theoretical and applied methods of microeconomics.
• Bouri, E., Kamal, E., and Kinateder, H. (2023). FTX Collapse and systemic risk spillovers from FTX Token to major cryptocurrencies. Finance Research Letters, 56, 104099.
• Campbell, J., Lo, A., and McKinlay, C. (1997): The Econometrics of Financial Markets, Princeton Univ. Press.
• Gallegati, M., & Semmler, W. (Eds.). (2014). Wavelet applications in economics and finance.
• Greene, W. H. (2018). Econometric Analysis. Pearson, eight edition.
• Kamal, E., and Bouri, E. (2023). Dependence structure among rare earth and financial markets: A multiscale-vine copula approach. Resources Policy, 83, 103626.
• Krolzig, H. M. (2000). Predicting Markov-switching vector autoregressive processes (pp. 1-30). Oxford: Nuffield College.
• Miťková, V. (2016) CGE Models Parameters Estimations Techniques. In: Ekonomické rozhľady 45, vol 1, s. 9-18. ISSN 0323-262X.
• Patton, A. J. (2012). A review of copula models for economic time series. Journal of Multivariate Analysis, 110, 4-18.
• Wooldridge, J. E. (2010) Econometric Analysis of Cross Section and Panel Data. 2nd Edition. South MIT Press.
50 points: semestral project on a selected topic
50 points: final oral exam,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
: Slovak and English
• Regression Analysis – Cross Section and Panel Data
• Instrumental Variables
• Randomized Controlled Trial
• Difference in Differences
• Regression Discontinuity Design
• Tail-risk dependence models
• Univariate and multivariate time series modelling - Volatility modelling – ARCH
• Wavelet analysis
• Markov-switching model
Upon completion of the course, the student:
• understands the theoretical and practical aspects of regression analysis,
• understands and uses the modern econometric approaches including randomized controlled trial and differences in differences to evaluate policies,
• uses and can design the financial econometrics models
Class syllabus:
• Basic conceptions of decisions making theory.
• Taxonomy of models and methods of optimization and multiple criteria decision making (MCDM).
• Efficiency and Non-dominance.
• Compromise programming and the best compromise alternative.
• Decision maker preference modeling.
• Selected applications of MCDM.
• Dynamic Games with Complete Information (Rationality, conflicts, and optimal decision-making under dynamic settings).
• Strategies, Equilibria, and Critiques of Multi-Stage Games.
• Applications in Repeated Games and Bargaining Models.
• Bayesian Games and Decision-Making Under Incomplete Information.
• Bayesian Games and Equilibrium Analysis.
• Dynamic Games of Incomplete Information.
• Zeleny., M.: Multiple Criteria Decision Making, McGraw – Hill Book Company, New York 1982.
• Steuer, R. E.: Multiple Criteria Optimization: Theory, Computation, and Application. John Wiley and Sons, New York, 1986.
• Mlynarovič, V.: Modely a metódy viackriteriálneho rozhodovania. Ekonóm, Bratislava, 1998.
• Fudenberg, D. and J. Tirole: Game Theory. Cambridge: The MIT Press, 1991. ISBN 978-0262061414.
• Osborne, M. J. and A. Rubinstein: A Course in Game Theory. Cambridge: The MIT Press, 1994. ISBN 978-0262650403.
• Tadelis, S.: Game Theory: An Introduction. Princeton: Princeton University Press, 2013. ISBN 978-0691129082.
• Varian, H. R.: Microeconomic Analysis. New York: W. W. Norton & Company, 1992. ISBN 978-0393960269.
50 points: semestral project on a selected topic (multiple criteria optimizations, outranking methods, game theory)
50 points: viva voice exam and/or written exam,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Basic conceptions of decisions making theory.
• Taxonomy of models and methods of optimization and multiple criteria decision making (MCDM).
• Efficiency and Non-dominance.
• Compromise programming and the best compromise alternative.
• Decision maker preference modeling.
• Selected applications of MCDM.
• Dynamic Games with Complete Information (Rationality, conflicts, and optimal decision-making under dynamic settings).
• Strategies, Equilibria, and Critiques of Multi-Stage Games.
• Applications in Repeated Games and Bargaining Models.
• Bayesian Games and Decision-Making Under Incomplete Information.
• Bayesian Games and Equilibrium Analysis.
• Dynamic Games of Incomplete Information.
After completing the subject, the student:
• understands the principles of multi-criteria optimization and has the ability to formulate and solve tasks of this type,
• acquires the ability to apply methods of complex evaluation of variants,
• understands current knowledge in the field of game theory and has the ability to find their application possibilities,
• • can create Excel applications for effective implementation of the methods in question.
• Aguiar, A., Chepeliev, M., Corong, E. and van der Mensbrugghe, D. (2022). The GTAP Data Base: Version 11. Journal of Global Economic Analysis, 7(2), 1-37. https://www.jgea.org/ojs/index.php/jgea/article/view/181
• Burfisher, M. (2021) Introduction to Computable General Equilibrium Models, Cambridge University Press.
• Dixon, P.B. and D.W. Jorgenson (2013): Handbook of Computable General Equilibrium Modeling, Elsevier.
• Miller, R.E. and P.D. Blair (2022): Input-Output Analysis – Foundations and Extensions, 3rd edition, Cambridge University Press.
• Miťková V. and Mlynarovič V. 2010. Makroekonomická analýza. Wolters Kluwer (Iura Edition).
• Miťková, V. and Kráľová, S. (2023) Wages and Income Impacts of a Liberal Migration Policy: An Experimental Study. Naše gospodarstvo/ Our Economy, 69(4), s. 38-50. DOI: 10.2478/ ngoe-2023-0022.
• Miťková, V. and Mlynarovič, V. (2021) Public Expenditures in the Selected Economic Industries: Policy Implications for the Period of the COVID-19 Pandemic Crisis. Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration 29(1), 1239. https://doi.org/10.46585/sp29011239
• Miťková, V. (2016) Lighting the Black Box of the Computable General Equilibrium Model Database. Domáca vedecká konferencia Quantitative Methods in Economics : Multiple Criteria Decision Making XVIII. Bratislava: Letra Interactive, 2016. S. 247-252. ISBN 978-80-972328-0-1.
50 points: semestral project on a selected topic (modelling final demand / final supply / labor market / climate change in CGE models
50 points: final oral exam,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Input-output tables. Multiregional input-output table construction. Social accounting matrix.
• Introduction to the theory of general economic equilibrium models.
• Parameters and calibration of the CGE model.
• Modelling of final demand in CGE models.
• Modelling final supply in CGE models.
• Modelling the labour market in CGE models.
• Modelling climate change in CGE models.
Upon completion of the course, the student:
• understands input-intput matrices and their extensions to multiregional I-O matrices,
• understands the basic structure of applied general equilibrium models,
• understands the basic theory of the standard general equilibrium economic model and the Global Trade Analysis Project model,
• can design and evaluate experiments of different policies
· Cunningham, S. (2021) Causal Inference: The Mixtape, Yale University Press, ISBN-10: 0
· Angrist, Joshua & Jorn-Steffen Pischke (2015). Mastering ’Metrics: The path from cause to effect. Princeton University Press.
· Chaisemartin, Clement de and Xavier D’Haultfoeuille (2020). “Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects”. In: American Economic Review 110.9, pp. 2964–2996.
· Calonico, Sebastian, Matias D Cattaneo, and Max H Farrell (2019). “Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs”. In: The Econometrics Journal 23.2, pp. 192–210.
· Kaminsky, G., and C. Reinhart. 1999. “The Twin Crises: The Causes of Banking and Balance-ofPayments Problems.” American Economic Review 89 (3): 473–500.
· Kaminsky, G., S. Lizondo, and C. Reinhart. 1998. “Leading Indicators of Currency Crisis.” IMF Staff Papers 45 (1): 1–48, International Monetary Fund.300251688, Online: https://mixtape.scunning.com/
· Sarlin, P. 2013. On Policymakers’ Loss Functions and the Evaluation of Early Warning Systems. ECB Working Paper Series No 1509, European Central Bank
· Sims, C.A. (1980) ‘Macroeconomics and reality’, Econometrica, Vol. 48, No. 1, pp.1–48.
· Sims, C.A., Stock, J.H. and Watson, M.W. (1990) ‘Inference in linear time series models with some unit roots’, Econometrica, Vol. 58, No. 1, pp.113–144.
50 points: semestral project
50 points: final oral exam,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Reflection of the most recent economic development in microeconomic research
• Revolution in econometric modelling - causal inference and impact evaluation
• Difference in differences (theory with applications)
• Instrumental variable regression (theory with applications)
• Regression discontinuity design (theory with applications)
• State of the art techniques of treatment effects identification (e.g., staggered treatment, PSM)
• Survival analysis and duration modelling (theory with applications)
• Early warning systems – signaling approach, probability models, policy maker utility functions
Upon completion of the course, students will learn about advanced microeconomic topics including real life application of these topics.
• Fisher Black, Michael Jensen, and Myron Scholes. The capital asset pricing model, some empirical tests. In Michael C Jensen, editor, Studies in the theory of capital markets. Preager, 1972.
• Eugene F Fama and J MacBeth. Risk, return and equilibrium, empirical tests. Journal of Political Economy, 81:607–636, 1973.
• John Y Campbell. Financial Decisions and Markets. A Course in Asset Pricing. Princeton University Press, 2018
• John Cochrane. Asset Pricing. Princeton University Press, revised edition, 2005.
• Wayne Ferson. Empirical Asset Pricing. Models and Methods. MIT Press, 2019.
• Bouri, E., Kamal, E., and Kinateder, H. FTX Collapse and systemic risk spillovers from FTX Token to major cryptocurrencies. Finance Research Letters, 56, 104099, 2023.
• Kiyotaki, N. Credit and business cycle. The Japanese Economic Review, 49/1, 1998.
• Kiyotaki, N. & Moore, J. Credit cycles. NBER Working Paper, 5083, 1995.
• Allen, F. & Gale, D. Financial contagion. Journal of Political Economy, 108/1, 2000.
• Borio, C. The financial cycle and macroeconomics: What have we learnt? BIS Working Paper, 395, 2012.
• Acharya, V. V. A theory of systemic risk and design of prudential bank regulation. Journal of Financial Stability, 5/3, 2009.
• Horvath, R., Horvatova, E., and Siranova, M. The determinants of financial development: Evidence from Bayesian model averaging, Economic Systems, 101274, 2024.
• Lučivjanská, K., Lyócsa, Š., Radvanský, M., and Širaňová, M. Return adjusted charge ratios: What drives fees and costs of pension schemes? Finance Research Letters, 48, August, 2022.
• Blake, D. Pension Finance. John Wiley and Sons, 2006.
• Bodie, Z., Treussard, J.. and Willen, P. The Theory of Optimal Life-Cycle Saving and Investing, The future of Life-cycle saving, The Research Foundation of CFA Institute, 2007, p. 19-37.
• Gajek, L., Ostaszewski, K. M. Financial risk management for pension plans, Elsevier, 2004.
• Zwecher, M. J. Retirement Porftolios – Theory, Construction, and Management. Wiley Finance, John Wiley and Sons, 2010.
• Balco, M., Sebo, J., Mestan, M. and Sebova, L. Application of the Lifecycle Theory in Slovak Pension System, Ekonomicky casopis, 66, 1, 64-80, 2018.
50% evaluation for a semester project on a chosen topic
50% evaluation for a final oral exam,
Point Assessment Final Evaluation
60 – 100 points "Passed"
0 – 59 points "Failed"
Slovak and English
• Theory of asset price modeling (APT, CAPM, yield curve)
• Systemic risk modeling in financial system (financial markets, financial institutions)
• Credit cycle and macroprudential policy
• Life-cycle investment strategy
• Pension systems modeling (PAYG, NDC, DCPS)
After completing the course, the student will acquire advanced knowledge in the field of financial asset pricing, systemic risk modelling and investment strategies of financial sector institutions. The student will be able to apply advanced measures of systemic risk to support the decision making process of policy makers. The student will gain the ability to test the effectiveness of forecasting techniques of asset price modelling in financial markets.
Recommended literature:
Auerbach, A.J., Gokhale, J. and Kotlikoff, L.J. (1991) ‘Generational Accounts: A Meaningful Alternative to Deficit Accounting’, Tax Policy and the Economy, 5, pp. 55–110. doi:10.1086/tpe.5.20061801.
Auerbach, A.J., Gokhale, J. and Kotlikoff, L.J. (1994) ‘Generational Accounting: A Meaningful Way to Evaluate Fiscal Policy’, Journal of Economic Perspectives, 8(1), pp. 73–94. doi:10.1257/jep.8.1.73.
Auerbach, A.J., Kotlikoff, L.J. and Leibfritz, W. (eds) (1999) Generational accounting around the world. Chicago: University of Chicago Press (A National Bureau of Economic Research project report).
Lee, R. and Mason, A. (eds) (2011) Population aging and the generational economy: a global perspective. Cheltenham: Edward Elgar.
Cunningham, S. (2021) Causal Inference: The Mixtape, Yale University Press, ISBN-10: 0
Kaminsky, G., and C. Reinhart. 1999. “The Twin Crises: The Causes of Banking and Balance-ofPayments Problems.” American Economic Review 89 (3): 473–500.
Kaminsky, G., S. Lizondo, and C. Reinhart. 1998. “Leading Indicators of Currency Crisis.” IMF Staff Papers 45 (1): 1–48, International Monetary Fund.300251688, Online: https://mixtape.scunning.com/
Sarlin, P. 2013. On Policymakers’ Loss Functions and the Evaluation of Early Warning Systems. ECB Working Paper Series No 1509, European Central Bank
Sims, C.A. (1980) ‘Macroeconomics and reality’, Econometrica, Vol. 48, No. 1, pp.1–48.
Sims, C.A., Stock, J.H. and Watson, M.W. (1990) ‘Inference in linear time series models with some unit roots’, Econometrica, Vol. 58, No. 1, pp.113–144.
Mojon, B. and Peersman, G. (2001) A VAR Description of the Effects of Monetary Policy in Individual Countries of the Euro Area, ECB Working Paper Series 2001-92, European Central Bank, Frankfurt
Project worth 50 % of the final grade and final exam worth 50 % of the final grade.
English
Causal inference and impact evaluation of public policies
• Regression based models in policy evaluation
• State of the art techniques of treatment effects identification
Generational economics
• Generational accounts and implicit debt of ageing
• National transfer accounts – life cycle deficit
European economic governance – empirical assessment
• Early warning systems – signaling approach, probability models, policy maker utility functions
• Macroeconomic policy impact evaluation – VAR models
Reflection of the most recent economic development in economic research
Students will learn about advanced economic topics of economic including real life application of these topic in policy making in EU countries.