International Economic and Financial Relations

The course primarily consists of three parts: an introduction to the problem of causality in economic and social sciences; an introduction to machine learning techniques; and an introduction to forecasting methods with time series. This is complemented by a laboratory for the presentation and study of the statistical software R. Detailed program: 1. Review of statistics and probability 2. Linear regression with a single regressor 3. Linear regression with multiple regressors 4. Non-linear regression functions 5. Panel data regression 6. Regression with binary dependent variable 7. Introduction to machine learning methods 8. Introduction to time series regression and forecasting The program is the same for both attending and non-attending students.

Statistics I

In-person

Two mandatory written tests are scheduled, each lasting one hour, covering the first and second parts of the program respectively. These tests will consist of exercises and open-ended questions. The oral exam accounts for half of the total score and involves the presentation of an academic article that uses one of the methods for evaluating public policies. The exam include the possibility of an essay that uses the R software on a specific dataset to be agreed with the teacher. Students can view the over 100 datasets in the MASS and AER libraries (both presented in class) and propose a topic to the teacher. Preferably students should work in groups of 2 or, alternatively, alone. The essay is worth up to a maximum of 3 additional points that are added to the exam grade

The program mainly consists of lectures, which are accompanied by classes and laboratories to develop the ability to use the appropriate statistical programs. In the lectures the main basic level tools used for estimating causal effects and accurate predictions will be presented and the critical interpretation of the results obtained will be stimulated. In the classes some exercises will be presented and discussed in preparation for the exam.