Econometrics (for math students & students following the Bachelor Economics & Mathematics)

Course goals

Learning objectives
At the end of the course the student is able to:

• understand the linear multiple linear regression model, including the OLS estimator and its statistical properties, functional form and model misspecification, and testing hypotheses;
• derive the OLS estimator and its covariance matrix by using linear algebra;
• derive the statistical properties of the estimator, and to understand the role of the necessary assumptions for these properties;
• 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 research question, research design and statistical hypothesis, and to test that hypothesis using regression analysis;
• assess the quality of the data used to address the empirical research question.

Content

This course cannot be combined with ECB2METRIE.

The discipline of econometrics is about the measurement of relationships between economic variables. For instance, researchers may be interested in the statistical association between the transaction price of a house and the size of the house. However, for a proper understanding of the housing market, researchers, policy makers and other stakeholders (e.g. homeowners and brokers) may want to go one step further. They want to have insight in the size of causal effects, such as the causal impact of the size of the house on its transaction price. Furthermore, a homeowner who wants to sell the house and who is ignorant about the right price, may want to predict the market price by using information of the size and all of the other features of the house.

To quantify an economic relationship, a regression analysis is the major statistical technique that is widespread used by economic researchers. Such an analysis starts with a dataset and an economic relationship. A linear regression equation is specified and the parameters can be estimated by the method of Ordinary Least Squares (OLS). The specific use of the estimation method depends on the structure of the data. The analysis includes an overview of the essential statistical assumptions that are needed for a proper economic interpretation of the parameter estimates. There are often empirical challenges, because the information included in the sample may not be fully suitable for a proper economic interpretation of the estimates. For instance, there may omitted variables in the regression equation and the variables may be contaminated by measurement error.

This course provides a thorough understanding of the linear regression model by considering all of the issues mentioned above. Knowledge of this course allows one to understand modern empirical economic literature. The linear regression model will be considered by linear algebra (matrices, vectors) and it will be used to derive the main estimators and hypothesis tests. In addition, the properties of these estimators (e.g. bias, consistency, and efficiency) will be considered. Finally, students will learn to assess the plausibility of the size of the parameter estimates.

The Real World Perspective
The course includes a group-based empirical research assignment in which students apply econometric methods to real-world economic and social questions. Students select a topic of their choice, formulate a research question grounded in economic theory, and analyse real data using linear regression techniques. Typical themes include education, labour markets, and social and gender inequality, while allowing flexibility in the choice of application. Through this assignment, students learn how econometric models are used in applied research and policy analysis, and how theoretical assumptions and data limitations affect empirical conclusions.

Format
One lecture and one tutorial per week. In addition, students will work on an empirical paper to practice with the methods.

Effort requirements

• Submission of a formative self-test in week 3 (not part of the final grade).
• Submission of the setup of the empirical paper in week 5 (see related documents on Brightspace).
• Submission of the final version of the empirical paper and a 15-minute group presentation (not part of the final grade) in week 9 (see related documents on Brightspace).
• Participation in two mandatory group meetings (online or in-person) in weeks 3 and 4 and weeks 6 and 7 to discuss progress and receive feedback.

Courses that build on this course:

• Applied Microeconometric Techniques (ECB3AMT)
• This course is optional for mathematics students

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 reusable 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.