79195 - Statistics for Economics

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Decent work and economic growth Reduced inequalities Responsible consumption and production Partnerships for the goals

Academic Year 2023/2024

Learning outcomes

By the end of the course, the students will have a good knowledge of the issues related to the measurement and modelling of economic behaviours, and more specifically: - the ability to provide a critical assessment on the quality and reliability of data from the main types of economic survey; - a good understanding of the specification and estimation techniques for a selection of statistical models grounded in economic theory; - a basic knowledge on the statistical models employed in the evaluation of economic policies with non-experimental data.

Course contents

The course is structure in two separate but strictly related modules.

Module 1 will cover the following topics:

  • Statistics for economics: questions, models, data types, measurement
  • Issues in estimating economic models: causality, endogeneity, corner solutions and censoring, selection biases, theoretical restrictions
  • Introduction to economic modelling with Stata

Module 2 will cover the following topics:

  • Applications in economics & business: modelling consumer demand; evaluating the impact of public policies; evaluating the effects of supermarket promotions; market forecasts
  • Policy evaluation with quasi-experimental data: Difference-in-Difference, Regression Discontinuity Designs, Instrumental Variables, Propensity Score Matching


The course will be mainly based on lecture notes and chapters/papers provided through the e-learning platform [https://virtuale.unibo.it/]

Some very useful reference books are:

Cameron, A. C. & Trivedi, P. K. (2022). Microeconometrics Using Stata. Volumes I and II. Second Edition. Stata Press.

Wooldridge, J.M. (2015). Introductory econometrics: A modern approach. South-Western Pub.

Angrist, J.D & Pischke, J-S. (2015). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.

Teaching methods

Each topic will be covered from both a theoretical and empirical perspective. Applications will be based on real data using the software STATA ®.

STATA is freely available to registered students, who can download the software and the license at this link using their student credentials.

This course aims to provide advanced empirical modelling and programming skills through a series of lab tutorials where students will be able to develop and run STATA codes under the lecturer direction and supervision. Furthermore data and codes will be provided on the e-learning platform Virtuale.

Assessment methods

Written exam.

The exam will be based on theoretical and empirical questions on topics and applications discussed during the course. The written exam is structured in two parts:

- A theoretical section, with multiple choice and/or open ended questions on topics covered during the course;

- An empirical question, where the student is presented with Stata codes and outputs produced during the course lab sessions, and is asked to interpret them and answer a set of questions

The two parts will equally weigh on the final mark.

Teaching tools

The Virtuale e-learning platform will provide students with:

- Lecture slides and notes

- Useful readings (articles, book chapters, etc.)

- Data, codes, short videos and materials for the Stata tutorials

- Exam-type questions

These materials will be provided and integrated throughout the course.

Office hours

See the website of Mario Mazzocchi

See the website of Beatrice Biondi