32626 - ECONOMETRICS

Conoscenze e abilità da conseguire

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.

Contenuti

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. Performe 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 the 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.
• Modification of the functional form (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. At this page [https://corsi.unibo.it/1cycle/Management/course-structure-diagram/piano/2024/5892/000/000/2024] take a look at the content of Statistics and Business Statistics.

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

Testi/Bibliografia

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.

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

Metodi didattici

To ensure a smooth transition from theory to practice in 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 and to model and estimate your own regression of interest, using the most appropriate methods according to the problem you face.

Modalità di verifica e valutazione dell'apprendimento

Some homework will be assigned during the course. These 'exercises' are intended to reinforce the concepts seen in class, to replicate, on new data, the empirical analyses carried out together, to familiarise you with the software and to prepare you for the final exam. You can work alone or in groups of up to 4 participants. Overall, homework counts for 40% of the overall assessment.

The remaining 60% of the assessment will be on an individual basis, through the final exam taken in class, after registration on AlmaEsami. The final test will take place on the EOL platform, where you will find a STATA dataset and a research question in a word file. it will therefore be entirely similar to the homework (mock exam) taken during the course.

Non-attending students will take a written examination based on the reference textbook.

The final grade may be:
30L excellent work!
28-30: you reveal independent knowledge and competence leading to good understanding and analytical performance.
24-27: the degree of autonomous knowledge is appreciable
18-23: Tasks are haphazard, with theoretical and methodological inaccuracies.
<18: incorrect or not handed in assignments.

Strumenti a supporto della didattica

Theoretical lectures are associated with working sessions; during them you will receive the suggestions needed to run your own empirical analysis. The data-sets and the programming files to perfom applied analyses will be provided during the lectures. The distributed material will be make available on the Virtuale platform. A virtual room on TEAMS will be available in case you cannot physically attend a lecture and to communicate via chat.

Software STATA: click here [https://scienzeaziendali.unibo.it/it/dipartimento/servizi-tecnici-e-amministrativi/software-con-licenze-campus]

Orario di ricevimento

Consulta il sito web di Maria Elena Bontempi