67730 - Econometrics

Learning outcomes

By the end of this course students are able to: - explain the fundamental principles of econometrics for cross-sectional data analysis; - interpret estimates from linear and non-linear regression models; - apply econometric techniques to analyze real-world micro-economic applications; - use software tools to perform empirical data analysis; - introduce a primer on causal inference and/or training on how to run an empirical project.

Course contents

This course is taught entirely in English.

Requirements

The course assumes familiarity with introductory calculus, linear algebra, probability, and statistics. Basic knowledge of microeconomics is recommended. All empirical applications are implemented using the statistical software R - this software is experimentally introduced in the Academic Year 2026-2027.

MAIN TOPICS

For the year 2026-2027 : Module 1 covers Part 1, 2, 3 and will be covered by Sergio Pastorello

Module 2 will cover Part 4 and Part 5.

Part 1: Introduction to econometrics

This part introduces the objectives of econometric analysis and the role of econometric models in economics. We discuss the distinction between descriptive, predictive, and causal analyses, review the basic concepts of probability and statistical inference, and introduce the workflow of empirical research.

Part 2: Linear regression models

Simple linear regression

We introduce the simple linear regression model, the Ordinary Least Squares (OLS) estimator, and its statistical properties. We discuss estimation, interpretation of regression coefficients, goodness of fit, and statistical inference through confidence intervals and hypothesis testing.

Multiple linear regression

We extend the regression framework to multiple explanatory variables. Topics include estimation and inference, model specification, interpretation of coefficients, categorical regressors, interaction terms, and the assumptions underlying OLS estimation.

Part 3: Regression models in empirical research

Model specification and evaluation

This part discusses how regression models are used in empirical economic research. We study functional form, nonlinear transformations, model diagnostics, and the interpretation of empirical results. Particular attention is devoted to assessing the credibility of regression-based studies and understanding the distinction between predictive performance and causal interpretation.

Part 4: Regression models for binary outcomes

Introduction to Maximum Likelihood estimation: empirical applications: frauds in the "Wheel of Fortune" game; testing whether the 'difficulty' of academic exams is constant across rounds

We study econometric models for binary dependent variables, focusing on their interpretation, estimation, and applications in economics (theory and applications might include modelling the choice between two brands)

 

Part 5: Introduction to econometric methods for causal inference

We will introduce the potential outcome framework, the definition of causal parameters of potential interest and a taxonomy of data and tools required to implement empirical strategies based on experimental or observational data that can retrieve causal parameters by reviewing some empirical examples.

Additional empirical applications will be covered. Some applications are taken from the books in the reference list.

Teaching material on computer lab exercises will be made available to students (also using the e-learning Platform https://virtuale.unibo.it/ )

Readings/Bibliography

Teaching material is based on selected material from the books listed below. Please contact instructors before buying the book.

Stock, J. H. and Watson, M. W. (2009) Introduction to Econometrics, 3e

Wooldridge, J. (2017) Introductory Econometrics: A Modern Approach, 7e

R. C. Hill, W. E. Griffiths, G. C. Lim, (2011) Principles of Econometrics (4th edition, International Student Version), Wiley

Joshua Angrist and Jörn-Steffen Pischke (2009) Mostly Harmless Econometrics: an empiricist's companion

Joshua Angrist and Jörn-Steffen Pischke (2015) Mastering 'Metrics: The Path from cause to effect

Franses, P.H. and Paap, R. (2007) Quantitative Methods for Marketing Research

All these should be available (at least in previous edition) from the University libraries. You can check availability from

http://sol.unibo.it/SebinaOpac/Opac?sysb=

Teaching methods

The course is only available in English

Lectures involve the presentation of theoretical and applied issues of the various econometric methods. Applications are discussed in class and replicated during the computer laboratory session using adequate software (for the academic Year 2026-2027: R is experimentally introduced).

I will experimentally adopt innovative teaching tools (such as peer instruction; see link with reference) using adequate techical support during lectures relying for instance on free available software such as Pingo (https://pingo.upb.de/) and Kahoot! (https://kahoot.com/). Students do not need to install the software ahead but need to have a device (mobile phone or laptop) who can access internet during the lectures in which this approach will be implemented.

In addition, peer education requires a great deal of investment from students as students have to read the textbook before coming to class.

As concerns the teaching methods of this course unit, all students must attend Module 1, 2 [https://www.unibo.it/en/services-and-opportunities/health-and-assistance/health-and-safety/online-course-on-health-and-safety-in-study-and-internship-areas] on Health and Safety online

Assessment methods

This course is taught entirely in English.

The final examination is designed to assess whether students have achieved the following learning outcomes:

• knowledge of the main econometric methods and techniques introduced during the course, including their theoretical foundations, appropriate applications, assumptions, and expected outputs;
• understanding of the fundamental distinction between econometric models used for prediction and those used for causal inference;
• ability to apply econometric methods to empirical problems involving both prediction and the estimation of causal effects.

Assessment consists of a written examination followed by an oral examination. The written examination is held in the computer laboratory, while the oral examination takes place shortly afterwards. The questions in both parts are randomly selected from a predefined pool; consequently, the specific content of the examination differs across students.

The written examination consists of multiple-choice questions and exercises requiring numerical answers to be computed using the R statistical software. The written examination is automatically graded. Students are admitted to the oral examination only if they achieve a passing grade on the written examination.

The oral examination consists of open-ended questions aimed at assessing students' understanding of the theoretical foundations of the methods covered in the course, their ability to interpret econometric results, and their capacity to discuss the assumptions and limitations of the techniques employed.

During both the written and oral examinations, students may not use calculators, mobile phones, smartwatches, or any other electronic devices, nor may they communicate with other candidates or consult notes, books, or any other written material. The use of artificial intelligence tools in any form is strictly prohibited. Any violation of these rules will result in exclusion from the examination.

The written examination lasts 60 minutes.

As an alternative to the final examination, students may take two midterm examinations. In this case, the final grade is the arithmetic average of the grades obtained in the two midterms. The dates of both the midterm and final examinations are fixed and cannot be rescheduled. Requests for additional examination dates will not be considered.

Students with specific learning disorders (SLD) and/or temporary or permanent disabilities are invited to contact the University office responsible for disability and inclusion services (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as early as possible. Requests for examination accommodations must be submitted to the instructor at least 15 days before the examination date. Accommodations will be granted in accordance with University regulations and the learning objectives of the course.

The maximum grade for both the written examination and each midterm examination is 30/30. The final grade is awarded after completion of both the written and oral examinations. The distinction 30 cum laude may be awarded only if the student's performance in the oral examination is outstanding.

Grades are interpreted as follows:

• <18: Fail
• 18–23: Satisfactory
• 24–27: Good
• 28–30: Very good
• 30 cum laude: Excellent

Students may reject the final grade only once. To do so, they must notify the instructor by email no later than the deadline for grade registration. The instructor will acknowledge receipt of the request.

Grade rejection always applies to the examination as a whole. For students taking the midterm option, this refers to the overall grade obtained by averaging the two midterm examinations. If the final grade is rejected, the student must retake the complete examination, including both the written and oral components.

Students who pass the first midterm may take the second midterm either during the first examination session immediately following the end of the course or during the subsequent examination session. The second midterm may be taken only once. If a student fails the second midterm or rejects the overall grade obtained through the midterm option, the student must take the complete final examination and forfeits the grade obtained in the first midterm.

The assessments methods of the course Intended Learning Outcomes (ILOs) are the following:

ILO1 Explain the fundamental principles of econometrics for cross-sectional data analysis: Multiple choice/Open-ended questions (to assess theoretical knowledge) - both oral and written part

ILO2 Interpret estimates from linear and non-linear regression models: Multiple choice/Open-ended calculation problems - both oral and written part

ILO3 Apply econometric techniques to analyze real-world micro-economic applications: Multiple choice/Open-ended questions (to assess theoretical knowledge) - both oral and written part

ILO4 Use software tools to perform empirical data analysis: Hands-on computer-based assessment - both oral and written part

ILO5 (if applicable): Introduce a primer on causal inference and/or training on how to run an empirical project: Case study analysis (multiple choice/open question ) - both oral and written part

Teaching tools

This course is taught entirely in English.

Students will also be requested to constantly test their understanding of the basic concepts discussed in class by means of quizzes that will be published on the on-line e-learning platform (Virtuale) at the end of each week. Quizzes are not compulsory, can be retaken as many times as students wish, and will not be corrected. The tutor of the course will be available one hour per week to answer questions on the quizzes.

Office hours

See the website of Margherita Fort