ECONOMETRICS AND BIG DATA
No prerequisites are required.
The exam is based on a written test lasting one and a half hours, consisting of 8-10 exercises of varying difficulty and scores. Some (4/5) questions are closed-ended, while the rest are open-ended. Incorrect answers do not result in a reduction in score. The exam rewards the ability to think critically and justify the chosen solution.
The advent of computers and the Internet has led to the availability of an enormous amount of data, in forms and quantities that were unthinkable just a few years ago. This phenomenon, sometimes called ‘big data’ or ‘data deluge’, is fulfilling H. G. Wells' prophecy that the ability to ‘think in terms of averages and maximums and minimums’ will be as necessary in the future ‘as it is now to be able to read and write’. This is especially true in economics. Therefore, the course aims to introduce students to the statistical study of the relationships between economic phenomena. At the end of the course, students are expected to be able to interpret the results of a regression model, conduct tests, and make predictions.
Examples of data. Probability, statistics, and programming in R. History of regression. The least squares method as a geometric tool. The simple regression model. The multiple regression model. The least squares method as a statistical tool. Properties of estimators. Predictions. Confidence intervals. Statistical tests.
The course will consist of lectures and distance learning with overhead projections (in English), exercises and programming examples using open source software, and material uploaded with the completion of some exercises. During classroom explanations, students are encouraged to interact with the instructor.
Student office hours by appointment, to be arranged via email at the instructor's institutional email address. Additional information about the course (such as the detailed class schedule and useful materials) will be provided on the e-learning platform.