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Syllabus

EN IT

Learning Objectives

Learning outcomes: The aim of this course is to introduce the students to state-of-the-art
techniques for microeconometric analysis, for both cross-sectional and longitudinal data.

Knowledge and understanding: Upon completing the course, students will be able to
understand microeconometrics tools. Specifically, the focus will be on linear static and
dynamic models for panel data and limited dependent variables models for cross-sectional
and longitudinal data. Emphasis will be given to the main assumptions for consistent
estimation and robust inference.

Applying knowledge and understanding: Upon completing the course, students will be able
to apply the discussed techniques using the Stata statistical software.

Making judgements: Upon completing the course, students will be able to orient themselves
between different techniques and choose the best for the case at hand.

Communication skills: Students will be able to present and effectively communicate the
results of their own elaborations and analyses.

Prerequisites

Students are expected to have prior knowledge from the courses in Mathematics (8011190), Statistics (8010848) and Econometrics (8011571). A basic familiarity with Stata is also required.

Program

A short introduction to Monte Carlo simulation (Lectures 1-2)

Linear panel data models (Lectures 3-10)
Static models and main assumptions
Fixed- and random-effects estimation
Homogeneity and specification tests
Extensions: Hausman and Tylor, Detrending
Difference-in-differences
Dynamic models

Models for limited dependent variables (Lectures 11–-18)
A short introduction to bootstrap, jackknife and delta methods for variance estimation
Binary outcomes
Multinomial outcomes
Count outcomes
Truncation and sample selection

Books

The main reference is Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).

For some topics, Wooldridge (2010) will be complemented by selected articles which will be made available on the material section of the course website and by some chapters from:

Greene W.H., Econometric Analysis, 8th ed., Pearson.

Bibliography

For further study in econometrics, I suggest Peracchi (2001), Davidson and MacKinnon (2004) and references therein:

Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
Davidson R. and MacKinnon J.G., Econometric Theory and Methods, New York, Oxford University Press, 2004.

Teaching notes and suggestions for further reading will be provided in class.

Teaching methods

The Microeconometrics course is delivered through face-to-face lectures and guided empirical sessions, combining theoretical exposition with hands-on data analysis. Instruction consists of 36 hours of teaching, scheduled according to the calendar published on the course website prior to the start of the semester.

Lectures develop the theoretical foundations of modern microeconometric methods, with particular attention to panel data models (static and dynamic), limited dependent variable models, and issues of endogeneity and sample selection. The theoretical content is continuously integrated with applied work, including interpretation of estimation output in Stata, Monte Carlo simulations, and replication of empirical results. This blended approach helps students internalize econometric reasoning and apply appropriate techniques to real-world economic data.

Exam Rules

The final exam consists of a 90-minute written test comprising three exercises. Two exercises assess theoretical understanding, requiring students to discuss and prove results or theorems presented during the course, as well as to analyze the assumptions and statistical properties of the estimation methods covered. The third exercise focuses on applied econometrics and involves interpreting Stata output, reporting estimation results, and evaluating hypothesis testing evidence.

To pass the exam, students must obtain a minimum score of 18 out of 33 on at least two of the three exercises. The final grade is calculated as the arithmetic average of the three individual scores. Each exercise is graded on a 0-33 scale, allowing students to attain a final mark of 30 even without achieving full marks on every exercise.
In addition, attending students are required to present, in pairs, a published academic paper that employs one of the econometric techniques discussed in the course. The presentation is graded on a scale from 1 to 4 points, which are added to the exam score (only for the exam session immediately following the course). Unjustified withdrawals from scheduled presentations will result in a penalty of 2 points.
Students must register for the final exam via the online portal https://delphi.uniroma2.it. Those who fail or choose to withdraw may retake the exam within the same examination session.

Grading Scale
Fail: Serious deficiencies in the understanding of core microeconometric concepts (e.g., panel data structure, main model assumptions). Inability to correctly apply estimation techniques or interpret results. Arguments are vague, incorrect, or unsupported by econometric reasoning.

18-20: Minimal understanding of key topics such as fixed- and random effects estimation, logit/probit and count data models. Application is basic and possibly incomplete. Reasoning shows gaps or uncertainty, but some ability to engage with the material is evident. Judgement is limited but sufficient for a passing grade.

21-23: Working knowledge of the discussed estimation methods. Able to correctly apply techniques and discuss core concepts. Arguments are logically structured, though lacking depth or nuance. Interpretation of empirical output is broadly accurate.

24-26: Good grasp of both theoretical underpinnings and applied aspects. Able to perform and interpret estimation procedures rigorously, including model diagnostics. Demonstrates a methodologically sound approach to empirical problems with clear and coherent argumentation.

27-29: Strong command of microeconometric tools, including advanced techniques (e.g., specification tests, dynamic panels, sample selection). Shows critical insight, clear economic reasoning, and the ability to independently evaluate assumptions and robustness. Effective use of Stata and confident interpretation of results.

30-30L (cum laude): Excellent mastery of the full range of econometric methods covered in the course. Demonstrates originality, precision, and depth in both theoretical and applied components. Exceptional analytical and problem-solving skills, with strong autonomy in integrating empirical findings with economic theory.