Data-driven economic analysis
A.A. 2023/2024
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 trimestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento può essere seguito come corso singolo.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo trimestre
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 consists 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. Additional activities for attending students will be communicated at the beginning of the course.
Module Economic Theory
Programma
1. Introduction to economic theory: basic principles and the role of models
2. Individual decision making: consumer theory and demand
3. Individual decision making: choice under uncertainty
4. Individual decision making: monopolistic pricing (linear pricing and price discrimination). Applications - two-sided markets and platforms.
5. Introduction to game theory: representations, definitions, equilibrium notions for static and multi-stage games.
6. Game theory applications: market interactions (price competition, product differentiation, capacity constraints); auctions; non-market interactions.
2. Individual decision making: consumer theory and demand
3. Individual decision making: choice under uncertainty
4. Individual decision making: monopolistic pricing (linear pricing and price discrimination). Applications - two-sided markets and platforms.
5. Introduction to game theory: representations, definitions, equilibrium notions for static and multi-stage games.
6. Game theory applications: market interactions (price competition, product differentiation, capacity constraints); auctions; non-market interactions.
Metodi didattici
Frontal lectures and exercises.
Materiale di riferimento
Teaching notes made available to students at the beginning of the course.
Additional textbooks (selected chapters) are:
Rubinstein, "Lecture notes in microeconomic theory"
Osborne and Rubinstein, "A course in game theory"
Other material for applications will be communicated at the beginning of the course.
Additional textbooks (selected chapters) are:
Rubinstein, "Lecture notes in microeconomic theory"
Osborne and Rubinstein, "A course in game theory"
Other material for applications will be communicated at the beginning of the course.
Module Econometrics
Programma
1. Economic Questions and the problem of causal inference
2. Simple regression model: Ordinary Least Squares (OLS) estimator and its properties
3. Multiple regression analysis: assumptions, estimation and inference
4. Limited dependent variable models: Logit and Probit models
5. Instrumental variables estimation and the Two Stage Least Squares (TSLS) estimator
6. Additional topics in Instrumental variables: specification tests and LATE.
7. Introduction to panel data models: fixed effects and random effects models
2. Simple regression model: Ordinary Least Squares (OLS) estimator and its properties
3. Multiple regression analysis: assumptions, estimation and inference
4. Limited dependent variable models: Logit and Probit models
5. Instrumental variables estimation and the Two Stage Least Squares (TSLS) estimator
6. Additional topics in Instrumental variables: specification tests and LATE.
7. Introduction to panel data models: fixed effects and random effects models
Metodi didattici
Frontal lectures and exercises.
Materiale di riferimento
Wooldridge J. "Introductory Econometrics"
Angrist J., Pischke J.S. "Mostly Harmless Econometrics"
Stock J., Watson M. "Introduction to Econometrics"
Angrist J., Pischke J.S. "Mostly Harmless Econometrics"
Stock J., Watson M. "Introduction to Econometrics"
Moduli o unità didattiche
Module Econometrics
SECS-P/02 - POLITICA ECONOMICA
SECS-P/05 - ECONOMETRIA
SECS-P/05 - ECONOMETRIA
Lezioni: 40 ore
Docente:
De Nadai Michele
Module Economic Theory
SECS-P/01 - ECONOMIA POLITICA - CFU: 6
Lezioni: 40 ore
Docente:
Zirulia Lorenzo
Siti didattici
Docente/i
Ricevimento:
14:00-17:00
via Teams/Zoom
Ricevimento:
Venerdì 9-12
Stanza 16, secondo piano, via Conservatorio 7/MS- TEAMS (previo appuntamento via mail in entrambe le modalità)