Unit ECONOMETRICS
- Course
- Quantitative finance and data science for economics
- Study-unit Code
- A003080
- Location
- PERUGIA
- Curriculum
- Quantitative and computational finance
- Teacher
- Barbara Guardabascio
- Teachers
-
- Barbara Guardabascio
- Hours
- 42 ore - Barbara Guardabascio
- CFU
- 6
- Course Regulation
- Coorte 2025
- Offered
- 2025/26
- Learning activities
- Caratterizzante
- Area
- Discipline economiche
- Academic discipline
- SECS-P/05
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- ENGLISH
- Contents
- The scientific method consists of four stages: observation, hypothesis, testing, and discussion. It is crucial to handle data competently in any scientific pursuit. If you have already a background in micro and macroeconomics, you are likely to have a solid understanding of how economists analyze the economy. However, it is important to question whether this approach truly reflects the workings of the world.
Econometrics is the fusion of economic theory and statistics used to test economic hypotheses derived from micro and macroeconomics.
The course goal is to familiarize students with the theory and use of quantitative methods in economics. The topics of the course are the linear model and its generalizations; estimation and testing theory; econometric specification techniques and model selection problems; instrumental variables, logistic regression, models for univariate time series. Such techniques are illustrated both theoretically and by means of empirical economic applications implemented using software such as Gretl or R. - Reference texts
- Reading material on each course topic (handouts, slides, data sets, R scripts), will be made available to the students by the course instructors during the course.
Suggested books are:
Jeffrey M. Wooldridge (2020), “Introductory Econometrics: A modern approach”, 7th Edition, Cengage Learning. - Educational objectives
- By the end of the module, students should:
Have a critical understanding of the nature of economic data;
Be able to abstract the essential features of an econometric issue or problem;
Be able to perform and critically evaluate model adequacy using relevant diagnostic and specification tests;
Have knowledge of different types of dynamic empirical model and their important role in the economic analysis of both longitudinal and time series data. - Prerequisites
- The advanced statistical methods introduced in the course are strongly dependent on basic mathematical and statistical tools, therefore it is expected that students had successfully attended and completed the planned mathematics and statistics courses.
- Teaching methods
- The course is carried out through lectures and practicums. Techniques will be introduced by examples and described in mathematical formulas. Focus will be on the practical implementation of each technique, and interpretation of results.
- Other information
- Attending classes is strongly advised.
- Learning verification modality
- During the course, students will be assigned a project aimed at assessing their skills in writing R code and interpreting the resulting outputs. The project will account for 40% of the final grade. Students who do not complete the project will be required to take an additional section during the exam, designed to evaluate the same competencies.
The exam will consist of a written test, which will contribute 60% to the overall grade. The test will include both theoretical questions and exercises involving numerical data analysis, with the aim of evaluating students’ analytical and interpretive abilities, as well as their understanding of the issues related to the application of the models covered in the course.
After grading, the instructor reserves the right to invite some students for an additional oral examination. - Extended program
- Linear Regression: Conditional expectation functions, multiple regression; OLS estimation: assumptions and properties; Residuals, fitted values, and goodness of fit; Diagnostic Tests.
Models for Binary Dependent Variables: Logistic Regression
Heteroskedasticity: Definition, Tests. Consequences of Heteroskedasticity for OLS estimation. The Weighted Least Squares Estimation. GLS and Feasible GLS.
Omitted Variable and Simple Regression Model. Endogeneity: definition and main causes. IV estimation as a general solution to the endogeneity problem. 2SLS Estimation and Test.
Introduction to time series
Time series definition: mean, variance, autocovariance and autocorrelation. Stationarity conditions. The Lag Operator. OLS assumptions with time series variables. The autoregressive models AR(p): estimation, model selection and forecasting. - Obiettivi Agenda 2030 per lo sviluppo sostenibile
- 4,8,9,12