Learning objectives
At the end of the course the student is able to:
• Understand the linear (bivariate and multivariate) regression model, including the ordinary least squares estimator and its statistical properties, functional form and model misspecification, and testing hypotheses;
• Specify, estimate, and interpret various cross-sectional and time-series regression models, and quantify the implications of an estimate for economic theory;
• Translate simple economic theory into a statistical hypothesis, and test that hypothesis using regression analysis;
Assess the quality of the data used to address the empirical research question
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Do economic conditions affect election outcomes? What is the monetary return to education? Do better-looking workers earn higher wages? Economists specify, analyse and quantify relationships between economic variables. The course Econometrics is a follow-up on the first-year course Statistics and introduces econometric (estimation) techniques that are useful for understanding both scientific articles and policy documents. Emphasis will be on the linear regression model (estimation, functional form, model selection, misspecification, and various tests), which will be applied to analyse data sets. In addition, attention is paid to time-series models and models with a binary dependent variable.
Multidisciplinary aspects
The course has multidisciplinary applications of econometric techniques included in both lectures and tutorials. Examples are the study of illegal markets (criminology & economics), gender violence in India (sociology & economics), the impact of physical attractiveness on wages (non-classical economics), labour market discrimination (sociology & economics), and the impact of economic conditions on re-election probabilities (political science & economics).
Students can also choose to write their paper on a multidisciplinary topic, such as the lasting impact of slavery on economic development (history, geography & economics); the determinants of happiness (sociology, psychology & economics); the extent to which prison sentences deter criminal behaviour (criminology, psychology & economics); awareness of GDPR and privacy rights (law & economics); opinions about vaccination programs and trust of government (sociology, political science & economics). Topic availability varies: datasets used for the paper change year by year.
Academic skills
This course focuses on the following academic skills:
Analytical skills
• Being able to solve problems (identifying the problem, devising a path towards the solution, following this path, verifying the outcome) for more complex assignments.
Communication skills
• Being able to write a compact and instructed paper on a specific subject, in groups.
• Being able to accept and process feedback from others on a draft paper.
Information processing
• Knowledge of potential sources for literature and data and the skills to explore these independently for a small research project.
• Being able to detect plagiarism and being able to avoid any kind of plagiarism.
• Providing correct references in text in a small research project (APA style).
• Providing a correct reference list for a small research project (APA style).
• Being able to present data in a correct and useful manner in a small and instructed research project.
• Being able to work with data software, such as Excel and Stata.
Academic research
• Being able to design a proper problem definition within the scope and instruction of the course.
• Being able to execute the instructed research design.
• Being able to search and select (additional) sources for the research.
• Being able to execute an instructed empirical research.
• Being able to apply theoretical concepts in research.
• Being able to use correct references (APA style).
• Being able to draw clear and correct conclusions.
Social responsibility
• Being able to work effectively in teams for a specific project with limited intervention or instruction.
Format
Lectures, tutorials, project groups.
Assessment method
60 % of the final grade: Basic material Econometrics: Formative assessment during the tutorials, which is not included in the grade. A summative individual exam in week 9 of the course. The exam consists of theory and applied econometric questions. Multiple choice questions make up a maximum of 25% of this examination.
40 % of the final grade: Quality of an Empirical project paper. In the first week, students form small project teams. Registration in a project group is done during the first tutorial: participation in the first tutorial is therefore obligatory. Formative assessment of the project during the meetings with the lecturer, and feedback on the draft paper in week 4 of the course.
Further information on assessment methods, academic skills, deadlines and procedures when having failed the exam(s) can be found on the course website and in the course manual.
Effort requirements
• Participation in at least 5 out of 8 tutorials, including the first tutorial;
• Participation in all project group meetings;
• Handing in a project report draft of a satisfactory level in week 4 of the course (see Blackboard and the course manual for a description of the requirements).
Course repeaters
Students who have failed this course in the previous academic year (either by failing the exams or by not having disenrolled within 2 weeks after commencement of the course) will take part in a repeater’s course, which consists of following the lectures. Repeaters are not enrolled in tutorials or paper groups.
The assessment method for repeaters is as follows:
• Repeaters do not write a project paper: their grade consists of the final exam (100%).
• There is no effort requirement.
• The grade conditions for the retake apply equally to repeaters.
Honours component
Honours students write a midterm paper in small groups on a more challenging dataset.
In case online access is required for this course and you are not in the position to buy the access code, you are advised to contact the course coordinator for an alternative solution. Please note that access codes are not re-usable meaning that codes from second hand books do not work, as well as access codes from books with a different ISBN. Separate or spare codes are usually not available.
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