Management Engineering

RICCARDO MARZANO

1) Knowledge and understanding
During the lessons of Data-driven Economics, students acquire the basic theoretical elements of econometric analysis. Theoretical lessons are aimed at guiding students in the acquisition of the basics of simple and multiple regression models, starting from the relative assumptions, and then proceeding with the estimation and inference procedures. The course contents cover both the estimation of linear and non-linear models and the analysis of both cross-sectional and longitudinal data.
2) Applicate applying knowledge and understanding
The students of the Data-driven Economics course are able to apply the notions acquired during the theoretical lessons to a wide range of problems of an empirical nature. They acquire the ability to build econometric models aimed at giving empirical content to economic relations and are also able to establish a causal link between two or more variables in the economic field.
3) Making judgements
Students are encouraged to critically discuss empirical studies published in the economic/managerial field in the classroom. The Data-driven Economics course also includes a laboratory in which students apply the acquired knowledge of econometrics to the estimation of empirical models carried out using data made available by the teacher.
4) Communication skills
At the end of the course, students are able to illustrate and explain the strengths and weaknesses of a wide range of empirical methodologies to a variety of heterogeneous interlocutors in terms of training and professional role. The acquisition of these skills is verified and evaluated not only during the final exam, by means of a written test and a possible oral test, but also during flipped class sessions in which, individually or in groups, students are called to present empirical studies published in the economic/managerial field.
5) Learning skills
Students acquire the ability to independently conduct empirical analyses by building econometric models to be estimated using data with diversified structures. The tools provided by the course allow for the analysis of systems in which a large number of factors simultaneously contribute to explaining their states and impact assessments that take into account the uncertainty and risk inherent in the application of policies. The acquisition of these skills is verified and evaluated on during the final exam, by means a written test and a possible oral test, in which the student can be called to discuss empirical problems on the basis of the topics covered and the reference material distributed during the course.

1) Knowledge and understanding
During the lectures of Data-driven Economics, students acquire the basic theoretical elements of econometric analysis. Theoretical lectures are aimed at guiding students in the acquisition of the basics of simple and multiple regression models, starting from the relative assumptions, and then proceeding with the estimation and inference procedures. The course contents cover both the estimation of linear and non-linear models and the analysis of both cross-sectional and longitudinal data.

2) Applying knowledge and understanding
The students of the Data-driven Economics course are able to apply the notions acquired during the theoretical lectures to a wide range of problems of an empirical nature. They acquire the ability to build econometric models aimed at giving empirical content to economic relations and are also able to establish a causal link between two or more variables in the economic field.

3) Making judgements
Students are encouraged to critically discuss empirical studies published in the economic/managerial field in the classroom. The Data-driven Economics course also includes a laboratory in which students apply the acquired knowledge of econometrics to the estimation of empirical models carried out using data made available by the teacher.

4) Communication skills
At the end of the course, students are able to illustrate and explain the strengths and weaknesses of a wide range of empirical methodologies to a variety of heterogeneous interlocutors in terms of training and professional role. The acquisition of these skills is verified and evaluated not only during the final exam, by means of a written test and a possible oral test, but also during flipped class sessions in which, individually or in groups, students are called to present empirical studies published in the economic/managerial field.

5) Learning skills
Students acquire the ability to independently conduct empirical analyses by building econometric models to be estimated using data with diversified structures. The tools provided by the course allow for the analysis of systems in which a large number of factors simultaneously contribute to explaining their states and impact assessments that take into account the uncertainty and risk inherent in the application of policies. The acquisition of these skills is verified and evaluated during the final exam, by means a written test and a possible oral test, in which the student can be called to discuss empirical problems on the basis of the topics covered and the reference material distributed during the course.

Fundamentals of Probability
Fundamentals of Mathematical Statistics
Matrix Algebra

Introduction to Econometrics and Economic Data
Simple Regression Analysis
Multiple Regression Analysis
Qualitative Information
Instrumental Variables Estimation
Panel Data Methods
Regression Discontinuity Design
Difference-in-differences and treatment evaluation
(Alternative Methods)

Primary:
Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 5th Edition, South-Western Cengage Learning
Secondary:
Joshua D. Angrist, & Jörn-Steffen Pischke, Mostly Harmless Econometrics, 2009 Edition, Princeton University Press

To be defined

Theoretical lessons
Lab sessions
Article presentations

In the classroom, twice a week

Students will be evaluated according to the following criteria:
Empirical project: 40%
Class participation: 10%
Oral exam: 50%

Data collection
Estimator choice
Choice of specification
Interpretation of results