Time series and dynamic panels 1600-SZD-SPEC-SC-EF
This course provides a dive into econometric techniques with a dual focus on time series analysis and dynamic panel data modeling. It begins by laying a rigorous theoretical foundation in time series methods, addressing issues of stationarity, unit roots, and cointegration, and advancing into model-building with ARMA, ARIMA, and VAR frameworks. Students will learn to diagnose and model structural breaks, handle nonlinear dynamics, and implement volatility models like ARCH and GARCH. The curriculum emphasizes the development of forecasting skills, ensuring that students are well-equipped to apply these techniques to analyze economic and financial phenomena.Building on the time series foundation, the course transitions into the realm of panel data econometrics, where traditional estimation methods are revisited and enhanced to address the complexities of dynamic panels. Advanced estimation techniques, such as the Arellano-Bond, System GMM, Pooled Mean Group, and Augmented Mean Group estimators, are thoroughly explored to tackle issues of endogeneity and heterogeneity across cross-sectional units. The course combines theoretical insights with extensive practical exercises using statistical software, enabling students to apply sophisticated econometric models to real-world data and critically evaluate their implications in fields such as macroeconomics, finance, and international economic policy.
Type of course
Course coordinators
Learning outcomes
Knowledge | The graduate knows and understands:
WG_01 - to the extent necessary for existing paradigms to be revised - a worldwide body of work, covering theoretical foundations as well as general and selected specific issues - relevant to a particular discipline within the social sciences
WG_02 - the main development trends in the disciplines of the social sciences in which the education is provided
WG_03 - scientific research methodology in the field of the social sciences
WK_01 - fundamental dilemmas of modern civilisation from the perspective of the social sciences
Skills | The graduate is able to:
UK_05 - speaking a foreign language at B2 level of the Common European Framework of Reference for Languages using the professional terminology
specific to the discipline within the social sciences, to the extent enabling participation in an international scientific and professional environment
Social competences | The graduate is ready to
KO_01 - fulfilling the social obligations of researchers and creators
KO_02 - fulfilling social obligations and taking actions in the public interest, in particular in initiating actions in the public interest
KO_03 - think and acting in an entrepreneurial manner
Assessment criteria
Description of requirements related to participation in classes, including the permitted number of explained absences: 80% participation
Principles for passing the classes and the subject (including resit session): Presentation of own research applying the methods discussed in the class
Methods for the verification of learning outcomes: Discussion of presentation of own research applying the methods discussed in the class
Evaluation criteria: Originality and appropriatness of the methods used in own research.
Bibliography
Enders, W. (2014). Applied Econometric Time Series (4th ed.). Wiley. Kilian, L., & Lutkepohl, H. (2017). Structural Vector Autoregressive Analysis. Cambridge University Press.A modern treatment of VAR and SVAR models, impulse responses, and applications in macroeconomics. Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. Ng, S. (2021). Recent Advances in Time Series Econometrics: Survey and Research Directions. Journal of Economic Literature, 59(1), 84-139.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: