Data-driven economic analysis
A.A. 2025/2026
Obiettivi formativi
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.
Risultati apprendimento attesi
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.
Periodo: Secondo quadrimestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo quadrimestre
Prerequisiti
A basic course in Statistics, including elements of inferential statistics. Knowledge of calculus, optimization theory and matrix algebra will also be required.
Modalità di verifica dell’apprendimento e criteri di valutazione
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
Programma
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
Metodi didattici
Frontal lectures and exercises.
Materiale di riferimento
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
Programma
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
Metodi didattici
Frontal lectures and exercises.
Materiale di riferimento
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"
Moduli o unità didattiche
Econometrics
SECS-P/02 - POLITICA ECONOMICA - CFU: 3
SECS-P/05 - ECONOMETRIA - CFU: 3
SECS-P/05 - ECONOMETRIA - CFU: 3
Lezioni: 40 ore
Docente:
Pignatti Morano Di Custoza Clemente
Economic Theory
SECS-P/01 - ECONOMIA POLITICA - CFU: 6
Lezioni: 40 ore
Docente:
Zirulia Lorenzo
Docente/i
Ricevimento:
Venerdì 9-12
Stanza 16, secondo piano, via Conservatorio 7/MS- TEAMS (previo appuntamento via mail in entrambe le modalità)