Course description
Many economic and financial variables are observed over time. In addition to being interested in the interrelationship among such variables, we are also concerned with relationships among the current and past values of one or more of them, that is relationship over time. Classical time series analysis can answer these important problems and hence is central to a wide range of applications including business cycle measurement, financial risk management, policy analysis, trend analysis, seasonality, analysis of structural changes, and forecasting. With the increasing availability of data, understanding the core methodologies is even more crucial to be able to properly work with the Big Data.
The objective of the course is to help students understand several important modern techniques in applied econometrics and time series. An emphasis of the course will be placed on understanding the essentials underlying the core techniques, and developing the ability to relate the methods to important issues faced by a practitioner. The course hence concentrates on the practical use of econometric methods, reviewing the relevant methodology, its use, and the possible alternative modeling approaches. The course will be oriented on computer classes where students can gain hands-on experience in applied econometric analysis. During the course we will especially focus on the basis of the classical time series analysis, multivariate time series, and modern extensions including connection to network analysis, time-varying parameter vector autoregressions, and connection to machine learning techniques, and Big Data estimation. The course will hence provide a state of the art overview of methodologies used in macro-finance modeling together with modern extensions.
Prerequisites
Prior experience with R-studio, basics of calculus, linear algebra, and statistics.
Course tools
R.
About the lecturer
Dr. Jozef Barunik
Jozef Barunik is Associate Professor at the Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, and is head of the Department of Econometrics at IITA, the Czech Academy of Sciences. His research has concentrated on the areas of financial econometrics, asset pricing, quantiles, frequency domain. He published the research results in well-established economic and financial journals such as Econometric Reviews, Journal of Financial Econometrics, Journal of Financial Markets, Quantitative Finance, Journal of Forecasting, Journal of International Money and Finance, or Journal of Economic Dynamics and Control. In addition to his academic activities, he actively works as an external consultant to major investment banks, consultancy businesses, as well as central banks.
Luboš Hanus
Luboš Hanus is a PhD candidate at the Institute of Economic Studies at Charles University. He also works as a junior researcher at the Institute of Information Theory and Automation at Czech Academy of Sciences.
