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2024/2025
1) Introduction to Econometrics (Master in Economics: Empirical Applications and Policies)
This course provides a basic-to-intermediate level introduction to the theory and practice of econometrics. The primary goal is to equip students with the fundamental knowledge and tools required to conduct empirical research in economics, assess government and business policies, perform economic forecasting, and utilise computational tools for regression analysis. Students will develop the skills necessary for comprehensive data analysis, which will be essential in various courses throughout the master's program, as well as in their final master’s thesis.
The course is structured to train students in exploring datasets, writing code to analyse relationships, and testing hypotheses related to economic phenomena. Students are introduced to the open-source statistical software R, where they will analyse different types of empirical datasets, apply various estimation techniques, and implement testing procedures in practical scenarios.
2) Econometrics (Master in Economics: Empirical Applications and Policies)
This is a course designed to equip students with the knowledge and tools necessary to conduct empirical research in economics, evaluate government and business policies, perform economic forecasting, and utilise computational tools for regression analysis. This course provides students with the essential techniques for data analysis, which they will apply throughout their master's program and in their final master's thesis.
Throughout the course, students will learn to address common econometric challenges such as heteroskedasticity, autocorrelation, endogeneity, selection bias, model misspecification, and measurement errors. The curriculum also expands the range of data analysis techniques by covering topics such as panel data and discrete choice models.
3) Microeconometría (Máster en Economía: Instrumentos del Análisis Ecónomico)
This course familiarises students with statistical and econometric methods used in analysing economic and social problems involving microeconomic units. A key focus is on causal choice models that describe the relationship between variables based on pre-existing behavioural theories. Additionally, students are introduced to different types of data commonly used in microeconometrics, such as sample selection data, panel data, and count data. The course emphasises practical applications and equips students with the necessary tools to analyse and interpret microeconomic phenomena using econometric techniques.
Students will explore a wide range of modern econometric techniques applied to economic data, covering discrete choice models, censored and truncated dependent variable models, two-step estimation methods, count data models, and panel data models. They will learn how to apply models such as Logit, Probit, Tobit, and Poisson regressions, while addressing key econometric challenges including sample selection, model specification, and estimation techniques. Practical applications using econometric software will be integrated throughout the course, reinforcing theoretical concepts through empirical exercises and real-world data analysis.
4) Upcoming summer schools
Spring School on Environmental Valuation using Discrete Choice Experiment, hosted by Università degli Studi di Messina, Italy.
The course will take place from May 26 to May 30, 2025
Instructors:
Maria De Salvo
Petr Mariel
Jürgen Meyerhoff
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