Data-Driven Economic Analysis
A.Y. 2025/2026
Learning objectives
The aim of this course is twofold. The first aim is to explain how economists take their theoretical models to the data. In particular, the course presents a set of basic economic models for the analysis of individual behaviour and market and non-market transactions, and illustrates which data are available to translate theoretical predictions into empirically testable research questions. The second aim is to analyse the main challenges faced by data scientists in answering empirical questions rooted in economic theory using data from standard and non-standard sources. The main emphasis will be on learning how to establish causal relationships between variables and how to exploit machine learning techniques to inform policy makers' decisions.
Expected learning outcomes
Upon completion of the course students will be able to:
1. understand basic economic models and data sources.
2. understand the issues involved in causal inference in the field of economics.
3. carry out regression analyses in Stata and interpret results.
4. apply basic machine learning techniques to assist causal inference.
1. understand basic economic models and data sources.
2. understand the issues involved in causal inference in the field of economics.
3. carry out regression analyses in Stata and interpret results.
4. apply basic machine learning techniques to assist causal inference.
Lesson period: Second four month period
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second four month period
Prerequisites for admission
A basic course in Statistics, including elements of inferential statistics. Knowledge of calculus, optimization theory and matrix algebra will also be required.
Assessment methods and Criteria
The exam is in written form only and lasts 90 minutes. It is divided in two parts, one for each module. Each part can consist of a mix of multiple-choice questions, open questions, and exercises, evaluating the skills and the critical abilities developed by the students as regards to the theories and the econometric methods explained during lectures. Students must pass both parts (mark greater or equal than 18/30) to pass the exam, with the final mark being the simple average of the marks in each part.
During the course, additional (non-compulsory) activities for attending students are held, whose rules are communicated at the beginning of the course by each instructor and published on the myAriel page of the course.
During the course, additional (non-compulsory) activities for attending students are held, whose rules are communicated at the beginning of the course by each instructor and published on the myAriel page of the course.
Economic Theory
Course syllabus
1. Introduction to the course
2. Preferences and utility
3. Utility maximization and choice
4. Income and substitution effects
5. Demand relationships among goods
6. Uncertainty
7. Game theory
8. Production functions
9. Cost functions
10. Profit maximization
11. The partial equilibrium competitive model
12. Monopoly
13. Imperfect competition
2. Preferences and utility
3. Utility maximization and choice
4. Income and substitution effects
5. Demand relationships among goods
6. Uncertainty
7. Game theory
8. Production functions
9. Cost functions
10. Profit maximization
11. The partial equilibrium competitive model
12. Monopoly
13. Imperfect competition
Teaching methods
Frontal lectures and exercises.
Teaching Resources
Teaching notes made available to students at the beginning of the course.
The textbook (selected chapters) is:
Nicholson, W., Snyder, C. (2024). "Microeconomic theory. Basic Principles and Extensions", Cengage, 12th Edition.
The textbook (selected chapters) is:
Nicholson, W., Snyder, C. (2024). "Microeconomic theory. Basic Principles and Extensions", Cengage, 12th Edition.
Econometrics
Course syllabus
1. Economic Questions and the problem of causal inference
2. Linear regression with one regressor: Ordinary Least Squares (OLS) estimator and its properties
3. Linear regression with multiple regressors: assumptions, estimation and inference
4. Non-linear regressions
5. Threats to internal validity of regressions and possible sources of bias
6. Regression with a binary dependent variable: Logit and Probit models and maximum likelihood estimation
7. Introduction to panel data models: fixed effects and random effects models
2. Linear regression with one regressor: Ordinary Least Squares (OLS) estimator and its properties
3. Linear regression with multiple regressors: assumptions, estimation and inference
4. Non-linear regressions
5. Threats to internal validity of regressions and possible sources of bias
6. Regression with a binary dependent variable: Logit and Probit models and maximum likelihood estimation
7. Introduction to panel data models: fixed effects and random effects models
Teaching methods
Frontal lectures and exercises.
Teaching Resources
Teaching notes made available to students at the beginning of the course.
Angrist J., Pischke J.S. "Mostly Harmless Econometrics"
Békés, G., Kézdi, G. "Data Analysis for Business, Economics, and Policy"
Stock J., Watson M. "Introduction to Econometrics"
Wooldridge J. "Introductory Econometrics"
Angrist J., Pischke J.S. "Mostly Harmless Econometrics"
Békés, G., Kézdi, G. "Data Analysis for Business, Economics, and Policy"
Stock J., Watson M. "Introduction to Econometrics"
Wooldridge J. "Introductory Econometrics"
Econometrics
SECS-P/02 - ECONOMIC POLICY - University credits: 3
SECS-P/05 - ECONOMETRICS - University credits: 3
SECS-P/05 - ECONOMETRICS - University credits: 3
Lessons: 40 hours
Professor:
Pignatti Morano Di Custoza Clemente
Economic Theory
SECS-P/01 - ECONOMICS - University credits: 6
Lessons: 40 hours
Professor:
Zirulia Lorenzo
Professor(s)
Reception:
Friday 9-12
Room 16, second floor, via Conservatorio 7/MS- TEAMS (please send me an email for booking a slot)