Data-Driven Economic Analysis
A.Y. 2023/2024
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 trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
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 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.
Data-Driven Economic Analysis-Module Economic Theory
Course syllabus
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.
Teaching methods
Frontal lectures and exercises.
Teaching Resources
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.
Data-Driven Economic Analysis-Module Econometrics
Course syllabus
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
Teaching methods
Frontal lectures and exercises.
Teaching Resources
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"
Data-Driven Economic Analysis-Module Econometrics
SECS-P/02 - ECONOMIC POLICY
SECS-P/05 - ECONOMETRICS
SECS-P/05 - ECONOMETRICS
Lessons: 40 hours
Professor:
De Nadai Michele
Data-Driven Economic Analysis-Module Economic Theory
SECS-P/01 - ECONOMICS - University credits: 6
Lessons: 40 hours
Professor:
Zirulia Lorenzo
Educational website(s)
Professor(s)
Reception:
2-5pm
on Teams/Zoom
Reception:
Friday 9-12
Room 16, second floor, via Conservatorio 7/MS- TEAMS (please send me an email for booking a slot)