Tipo Insegnamento:
Opzionale
Durata (ore):
60
CFU:
9
SSD:
ECONOMETRIA
Sede:
BRESCIA
Url:
BANCA E FINANZA/Piano generale Anno: 3
Anno:
2024
Course Catalogue:
Dati Generali
Periodo di attività
Secondo Quadrimestre (27/02/2025 - 09/06/2025)
Syllabus
Obiettivi Formativi
Knowledge and understanding: at the end of the course, students are expected to know and understand the main hypothesis underlying the basic econometric techniques used for the analysis of cross-sectional, time-series and panel data.
Applying knowledge and understanding: at the end of the course, students are expected to know the application domain of the main econometric techniques. Students are also expected to know how to practically implement the basic econometric techniques using the R statistical software.
Making judgements: at the end of the course, students are expected to to evaluate the empirical analysis of others, interpret results, and draw lessons. They are expected to autonomously formulate simple research questions, apply the basic econometric techniques to real data to provide empirical answers, using the R software,
Communication skills: at the end of the course, students are expected to communicate the acquired knowledge with clarity and effectiveness, using the vocabulary of econometrics.
Learning skills: at the end of the course, students must have developed good learning skills, which allow them to continue their study independently and to apply the knowledge acquired in both the professional and academic contexts.
Applying knowledge and understanding: at the end of the course, students are expected to know the application domain of the main econometric techniques. Students are also expected to know how to practically implement the basic econometric techniques using the R statistical software.
Making judgements: at the end of the course, students are expected to to evaluate the empirical analysis of others, interpret results, and draw lessons. They are expected to autonomously formulate simple research questions, apply the basic econometric techniques to real data to provide empirical answers, using the R software,
Communication skills: at the end of the course, students are expected to communicate the acquired knowledge with clarity and effectiveness, using the vocabulary of econometrics.
Learning skills: at the end of the course, students must have developed good learning skills, which allow them to continue their study independently and to apply the knowledge acquired in both the professional and academic contexts.
Prerequisiti
Basic elements of the theory of probability, introductory statistics, statistical inference, and basics of R programming are necessary to understand the course. They are taken for granted.
Metodi didattici
In-class lectures are complemented by tutorials conducted using the R software. The integration of econometric theory and empirical applications is emphasized throughout. Each topic covered in class is accompanied by a dedicated tutorial session. These tutorials not only provide an opportunity to gain proficiency with the software but also facilitate familiarity with the research process, spanning from data cleaning to the interpretation of results.
Understanding in empirical economics and econometrics builds progressively throughout the course. As such, students are encouraged to keep pace with lectures and study materials as they are presented.
Work in groups is required to develop the empirical project.
Should questions or uncertainties arise, students are encouraged to attend the professor's office hours or reach out to either the professor or the teaching assistant for additional clarification and support.
Understanding in empirical economics and econometrics builds progressively throughout the course. As such, students are encouraged to keep pace with lectures and study materials as they are presented.
Work in groups is required to develop the empirical project.
Should questions or uncertainties arise, students are encouraged to attend the professor's office hours or reach out to either the professor or the teaching assistant for additional clarification and support.
Verifica Apprendimento
The verification of learning is in two parts: a written exam and the presentation of a group project. Each part accounts for 50% of the final grade.
• The written exam is composed of two exercises with sub-questions. Each exercise provides preliminary information about an empirical application. Questions will test the ability to understand and interpret the available information and use it to make further analysis. The exam is open book.
• The second part is in two steps:
(i) the preparation of an empirical project in groups (3-4 people) where an empirical question is formulated and addressed, applying an econometric technique using data with the software R.
(ii) the presentation of the project in 15 minutes. During the oral presentation, the professor might also ask additional theory questions related to the program. The presentation will be assessed according to the following criteria: clear hypothesis formulation, correct method application and interpretation, critical assessment, and effective communication. A list of possible topics for the projects will be provided to the students throughout the course, but students can propose new topics according to their interests.
The exam is passed if the average grade (0.5 × written exam + 0.5 × group project) is greater or equal than 18. However, the grade in each of the two parts must be greater or equal than 15.
• The written exam is composed of two exercises with sub-questions. Each exercise provides preliminary information about an empirical application. Questions will test the ability to understand and interpret the available information and use it to make further analysis. The exam is open book.
• The second part is in two steps:
(i) the preparation of an empirical project in groups (3-4 people) where an empirical question is formulated and addressed, applying an econometric technique using data with the software R.
(ii) the presentation of the project in 15 minutes. During the oral presentation, the professor might also ask additional theory questions related to the program. The presentation will be assessed according to the following criteria: clear hypothesis formulation, correct method application and interpretation, critical assessment, and effective communication. A list of possible topics for the projects will be provided to the students throughout the course, but students can propose new topics according to their interests.
The exam is passed if the average grade (0.5 × written exam + 0.5 × group project) is greater or equal than 18. However, the grade in each of the two parts must be greater or equal than 15.
Testi
Stock, J. H. and M. W. Watson (2020). Introduction to Econometrics (4th, global ed.). Pearson – Milan; (main reference).
Békés, Gábor and Gábor Kézdi (2021). Data analysis for business, economics, and policy. Cambridge University Press – Cambridge. ISBN: 978-1-108-71620-8.
Békés, Gábor and Gábor Kézdi (2021). Data analysis for business, economics, and policy. Cambridge University Press – Cambridge. ISBN: 978-1-108-71620-8.
Contenuti
The course offers a first exploration of fundamental techniques for estimating causal effects using observational data and for prediction and forecasting. Students will develop familiarity with key econometric methodologies for analyzing cross-sectional, time-series, and panel data.
In the initial segment of the course, emphasis is placed on a cross-sectional approach, beginning with the introduction of the univariate linear regression model and hypothesis testing. This model is then expanded to encompass the multivariate case, along with its application in nonlinear contexts.
The subsequent segment of the course extends the regression framework to address constraints pertaining to the internal validity of the linear regression model. It explores various solutions through the introduction of panel data, nonlinear models, and instrumental variables regression models. A preliminary discussion on program evaluation, incorporating experiments and quasi-experiments, is also provided.
In the final part of the course, attention shifts towards the time-series dimension for estimating causal effects and for prediction. This section introduces students to autoregressive processes and dynamic causal effects.
Throughout the course, the presentation of econometric models is motivated by real-world applications and supported by practical tutorials.
In the initial segment of the course, emphasis is placed on a cross-sectional approach, beginning with the introduction of the univariate linear regression model and hypothesis testing. This model is then expanded to encompass the multivariate case, along with its application in nonlinear contexts.
The subsequent segment of the course extends the regression framework to address constraints pertaining to the internal validity of the linear regression model. It explores various solutions through the introduction of panel data, nonlinear models, and instrumental variables regression models. A preliminary discussion on program evaluation, incorporating experiments and quasi-experiments, is also provided.
In the final part of the course, attention shifts towards the time-series dimension for estimating causal effects and for prediction. This section introduces students to autoregressive processes and dynamic causal effects.
Throughout the course, the presentation of econometric models is motivated by real-world applications and supported by practical tutorials.
Lingua Insegnamento
English
Corsi
Corsi
BANCA E FINANZA
Laurea
3 anni
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