B5558 - STATISTICS FOR MODELLING ECONOMIC BEHAVIOURS

Learning outcomes

By the end of the module, students will be acquainted with the main behavioural economic theories, with the appropriate data sources and statistical models to analyse them, and with the potential public and private strategies drawing from behavioural evidence, with a specific focus on consumer behaviour.

Course contents

The course reviews the key elements of behavioral economics and policy under an applied perspective, covering issues related to statistical measurement and modelling of economic behavior, with a specific focus on consumer behaviour.

• Rationality, irrationality and limited rationality of economic agents
• Fast and slow thinking systems
• Heuristics and behavioral biases
• Statistical measurement of preferences and choices
  • Stated vs. revealed preferences
  • Artificial intelligence and measurement
  • Primary surveys and field work for behavioural data
• Statistical modelling of economic behaviours
  
  • Discrete choice models
  • Panel data models
  • Quasi-experimental methods
• Applications and case studies
  • The applications will be defined during the course in consultation with the students. They may be proposed by the instructor or jointly agreed upon and/or modified with the students.
  • The data used in the applications may come from secondary sources (real-world data) or may be generated using AI, once the characteristics of the dataset and the variables it contains have been defined. In all cases, the data collection method and any measurement biases relevant to the specific application will be explicitly discussed.

Readings/Bibliography

The course is based on lecture notes and chapters/articles provided through the e-learning platform

Teaching methods

The course consists of a combination of theoretical lectures, applied case studies and quantitative tutorials using real data and the statistical software Stata. No preliminary knowledge of Stata is required to take the course, and students will be provided with introductory materials.

Assessment methods

• During the course, students will be provided with case studies, data and research questions and will be required to write a short report to be delivered and presented by the end of the course

• For those students who do not deliver the essay, or fail, it will be possible to take a written exam with multiple-choice and open-ended questions, including the interpretation of findings from a case study.
  

Report and presentation for attending students: As part of the assessment, the report and presentation include a component in which substantial use of AI may be made—for example, to formulate problems, generate data, or produce content—and a compulsory critical analysis component.

Written examination: The use of AI is prohibited in the written examination. Any use of AI constitutes a breach of academic integrity.

Teaching tools

This module provides a set of case studies on statistical models of economic behaviors. The e-learning platform will enable access to interactive contents, data and case studies, Stata codes.

Office hours

See the website of Mario Mazzocchi