ECRMRS15 ECTSQ2EnglishMaster
Econometric Methods 2 + Research skills: Data handling
FaculteitFaculty of Law, Economics and Governance
NiveauMaster
Studiejaar2026-2027
Beschrijving
Course goals
Learning objectives
At the end of the course, the student is able to:
- handle a set of raw data in such a reliable and transparent way
- understand the issue of causality in terms of endogeneity and selectivity of the data
- select the statistical methods that leads to a causal interpretation of the parameter estimates
- apply the estimator in Stata
- interpret the estimation results.
Content
Students who are not enrolled in the research master Multidisciplinary Economics should contact the course coordinator for enrollment in this course.
This course is the second of two required courses on the main econometric techniques that are used in empirical research in economics. The course aims to discuss broadly applied methods of micro- econometrics that are used to deal with endogeneity, selectivity, and specific data structures in empirical research. Applications with examples of research will be examined by using Stata.
In addition, the course aims to provide a research skill - the handling of datasets which is usually the start of a research project. After an introduction to the issues of data management, students will create their own data set that can be used for empirical research. Starting from a raw data set, students will learn how data cleaning can be done in a transparent way, such that that it can be replicated by other researchers and that it results in a data set that can be used for the application of the estimation techniques.
The econometric issues that will be examined are the following.
Part 1 (4 weeks): maximum likelihood, endogeneity, IV and 2SLS, limited dependent variables, generalised methods of moments; Part 2 (4 weeks): causality, conometric design and potential outcomes (dif-in-dif; RD, propensity score and matching), duration analysis, count data models, quantile regression.
Intended Learning Outcomes
The graduate:
This course is the second of two required courses on the main econometric techniques that are used in empirical research in economics. The course aims to discuss broadly applied methods of micro- econometrics that are used to deal with endogeneity, selectivity, and specific data structures in empirical research. Applications with examples of research will be examined by using Stata.
In addition, the course aims to provide a research skill - the handling of datasets which is usually the start of a research project. After an introduction to the issues of data management, students will create their own data set that can be used for empirical research. Starting from a raw data set, students will learn how data cleaning can be done in a transparent way, such that that it can be replicated by other researchers and that it results in a data set that can be used for the application of the estimation techniques.
The econometric issues that will be examined are the following.
Part 1 (4 weeks): maximum likelihood, endogeneity, IV and 2SLS, limited dependent variables, generalised methods of moments; Part 2 (4 weeks): causality, conometric design and potential outcomes (dif-in-dif; RD, propensity score and matching), duration analysis, count data models, quantile regression.
Intended Learning Outcomes
The graduate:
- has broad knowledge of core and state-of-the-art theories and empirical methods in economics
- can formulate a data management plan, and manage data sets, using statistical software
- can apply acquired knowledge of theories and empirical methods within economics
- has a sharp sense of academic integrity and demonstrates being a responsible and scholarly professional
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