32626 - Econometrics

Learning outcomes

Nowadays, applied work in business management requires a solid understanding of econometric methods to support decision making. The course aims at deepening theoretical and practical knowledge of the methods for conducting empirical research through the specification of a testable empirical econometric model; the estimation of unknown parameters based on observed data (cross-section and panel data); the evaluation of the nature of the error term and hyptheses testing; the conditions to obtain sensible results with a causal interpretation; how to critically understand empirical articles. Data samples and an econometric software will be used to estimate and evaluate models during hands-on sessions. The course in Econometrics has been design to be integrated with the course in Business Statistics which introduces to the statistical approach to extract information from data. The two courses compose the course in “Quantitative methods for management I.C.”, and provide the student with a quantitative toolbox to support data-driven decision making.

Course contents

Learning objectives: at the end of this course, students will be able to: 1. Understand the role and importance of econometrics in economic analysis. 2. Formulate and estimate linear regression models. 3. Perform hypothesis testing and interpret statistical significance. 4. Identify and correct common econometric problems such as multicollinearity, heteroskedasticity, and endogeneity. 5. Use econometric software (Stata) for empirical analysis.

Course outline:

• What is econometrics? The research question, definition and scope of econometrics. The different types of data: cross-sections, time-series, panel data.
  
• Understanding data through exploratory data analysis: distributions, tables, histograms, scatterplots, hypothesis testing and joint hypothesis testing.
  
• The classical linear regression model (CLRM) and the OLS estimator: assumptions and properties of the method.
• Application of OLS to the simple regression model: validation of the method through specification tests on residuals; interpretation of the coefficients derived from the estimation, confidence intervals, goodness of fit.
• Alternative functional forms (logarithms and quadratic forms).
• Dummy variables and interaction terms: categorical variables in regression; interaction effects and interpretation.
• Extension to multiple regression models: interpretation of coefficients; omitted variables bias; multicollinearity and its consequences.
• What to do when OLS assumptions are no longer valid? Heteroskedasticity is an example of OLS assumption violation: detection and remedies. Robust standard errors. Generalised least squares.
• Another violation of OLS assumptions: endogeneity of explanatory variables. Sources of endogeneity. The instrumental variables approach.

Clearly there are necessary prerequisities, specifically for Erasmus students:

1. Take a look at the content of Statistics and Business Statistics.

2. Basic familiarity with Stata is recommended. An introduction to the software will be provided during the course.

Readings/Bibliography

The material (articles, commented notes & slides, programs and data-sets) will be distributed during the lectures and make available on the platform Virtuale.
The reference textbook is:
Wooldridge J.M. 2020 Introductory Econometrics. A Modern Approach, Cengage, 7th Edition.

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 econometrics, theoretical lectures are combined with working sessions. During the practical empirical applications, students will work with the econometric software Stata.

At the end of the course, students will be able to develop quantitative reasoning and interpret empirical evidence.

Assessment methods

The integrated course Quantitative Methods for Management consists of two modules:

• Business Statistics
• Econometrics

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.

Assessment in the Econometrics module consists of:

• two research assignments (continuous assessment);
• an individual written examination.

Continuous assessment

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

Since the Econometrics module is delivered in two teaching periods, two research assignments are scheduled during the course.

The first assignment must be submitted and assessed no later than the March examination session immediately following the first teaching period.

The second assignment must be submitted and assessed no later than the first June examination session immediately following the second teaching period.

Both assignments are valid only for the academic year in which they are assigned. After the relevant examination session, the continuous assessment expires and cannot be carried forward to subsequent examination sessions or academic years.

The two assignments each contribute 20% of the module grade. They may be completed individually or in groups of up to four students. Students working in groups must maintain the same group composition for both assignments.

The assignments are 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.

Students using Artificial Intelligence tools (e.g. ChatGPT, Claude, Gemini, Copilot or similar systems) must disclose their use in a short appendix describing:

• the AI tool(s) used;
• the main prompts or questions submitted;
• how the AI-generated outputs contributed to the assignment;
• a brief critical evaluation of their 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 students' understanding of simple and multiple regression models, alternative functional forms, specification tests, 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 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