B1894 - Econometrics for Management

Learning outcomes

The integrated course Data Analysis for Managerial Decision-Making provides students with the theoretical and practical skills necessary to analyse and interpret empirical data using econometric methods. Once the basics have been consolidated, more advanced methods are introduced, focusing on panel data, which is now a cornerstone of modern economic analysis. The main objective of the course is to provide students with the techniques necessary to conduct rigorous empirical research, in which critical thinking, knowledge of econometric software and data-driven decision-making enable them to tackle complex economic and sustainability challenges in managerial contexts.

Course contents

The schedule of the program goes along the following points:

1. The IV approach.
2. Introduction to panel data; longitudinal data, cross-section time-series data, multilevel data.
3. The fundamental role of heterogeneity: how to deal with it.
4. Static and linear panel data models: two or more levels, the error components.
5. Estimation methods: Pooled OLS; fixed and random effects; between effects; first differences.

There are clearly some prerequisites, especially for Erasmus students:
1. You must have attended the Data Mining course or be familiar with its contents. It is also useful to take a look at the contents of the courses in Statistics, Business Statistics and Econometrics.

2. A knowledge, at least basic, of STATA software is also required.

Readings/Bibliography

The material (articles, commented notes & slides, Stata scripts and data-sets) will be distributed during the lectures and make available on the platform Virtuale.

The textbook is Wooldridge J.M. 2020 Introductory Econometrics. A Modern Approach, Cengage, 7th Edition, Ch 13-15.

For an overview of Stata: Baum, C. F. (2006) An Introduction to Modern Econometrics Using Stata, Stata Press. Why programming in Stata? Have a look at Cox N. J. (2001) Speaking Stata: How to repeat yourself without going mad, The Stata Journal, 1, Number 1, pp. 86–97.

Teaching methods

To ensure a smooth transition from theory to practice in panel data econometrics, theoretical lectures are combined with working sessions. During the practical empirical applications, you will use the computer and Stata econometric software (available with a CAMPUS licence and your university credentials).
At the end of the course, you will be able to critically evaluate articles that present basic empirical analyses on panel data and to model and estimate your own regression of interest, using the most appropriate methods according to the problem you face.

Assessment methods

The integrated course Data Analysis for Managerial Decision-Making consists of two modules:

Data Mining
Econometrics for Management

Each module includes

• a research assignment (continuous assessment);

• an individual written examination.

The final grade for the integrated course is calculated only after both modules have been successfully completed, according to the weighting established for each module.

Continuous assessment
Students may choose to participate in the continuous assessment offered during the teaching period.

The continuous assessment consists of one empirical project, to be completed individually or in groups of up to four students, and accounts for 40% of the final grade for the respective module.

The project is designed to reinforce the concepts covered during the lectures through the analysis of empirical data using Stata. Students are expected to justify their methodological choices, interpret their results, and critically discuss their findings.

The project forms an integral part of the learning activities carried out during the course. Consequently, it is valid only for the academic year in which it is assigned. For the Econometrics for Management module, the project must be submitted and assessed no later than the first June examination session immediately following the end of the course.

Students enrolled in the integrated course should refer to the Data Mining syllabus for the corresponding deadline applicable to that module.

After the relevant examination session, the continuous assessment expires and cannot be carried forward to subsequent examination sessions or academic years.

The use of Artificial Intelligence tools (e.g. ChatGPT, Claude, Gemini, Copilot or similar systems) is permitted as a support for learning. Students remain fully responsible for the submitted work and must disclose any AI assistance. Reports produced with AI support must include a brief appendix describing:

the AI tool(s) used;
the prompts or prompting strategy employed;
how the AI-generated output was used;
a critical evaluation of its usefulness, accuracy and limitations.

The purpose of allowing AI tools is to develop students' ability to critically evaluate, verify and improve AI-generated suggestions through independent econometric reasoning, rather than replace their own analysis.

Failure to disclose AI assistance may be treated as a breach of academic integrity.

All empirical assignments must be fully reproducible. Students are expected to submit the complete Stata do-files required to generate all tables, figures and results presented in their reports.

Written examination
The written examination accounts for 60% of the final grade for students who complete the continuous assessment.

It is delivered through the EOL platform and consists of the interpretation of Stata output together with open-ended questions assessing the student's understanding of instrumental variables, panel-data methods, and the econometric techniques covered during the course.

Students who do not complete the continuous assessment within the prescribed period, or who choose not to participate in it, will be assessed through a written examination covering the entire syllabus of the Econometrics for Management module. In this case, the written examination determines 100% of the final grade for the module.

The final grade may be:
30 cum laude outstanding performance demonstrating complete mastery of the subject together with excellent analytical and interpretative skills.
28-30 excellent knowledge of the subject and very good analytical skills.
24-27 good knowledge of the subject with appropriate methodological understanding.
18-23 satisfactory performance despite theoretical or methodological weaknesses.
<18 insufficient achievement of the learning outcomes.

Teaching tools

Theoretical lectures are complemented by practical laboratory sessions, during which students receive guidance on implementing empirical analyses using Stata. Datasets and programming files required to perform the empirical applications will be provided during the course. All teaching materials, including slides, datasets, programming files and supplementary notes, will be made available on the Virtuale platform.

A Microsoft Teams virtual classroom will also be available for students who are exceptionally unable to attend a lecture in person and for communication outside class.

Stata software: students can access Stata free of charge through the University CAMPUS licence using their institutional credentials:
https://www.unibo.it/secure/software-stata/

Office hours

See the website of Maria Elena Bontempi