Scheda programma d'esame

TOPICS IN MICROECONOMETRICS

ANGELA PARENTI

Academic year2023/24

CourseECONOMICS

Code611PP

Credits6

PeriodSemester 1

LanguageEnglish

CourseECONOMICS

Code611PP

Credits6

PeriodSemester 1

LanguageEnglish

Obiettivi di apprendimento

Learning outcomes

Conoscenze

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Knowledge

Part I (Prof. Paolo Frumento)

Students are expected to acquire familiarity with a variety of regression models, to perform estimation and inference, to understand the role of covariates, and to utilize the R statistical software.

Part II (Prof.ssa Angela Parenti)

Students will learn different microeconometrics techniques, ranging from the main empirical strategies for causal inference to spatial econometrics.

Modalità di verifica delle conoscenze

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Assessment criteria of knowledge

Part I (Prof. Paolo Frumento)

Progress will be assessed during the course, through interaction with the students. A final exam will be performed using R.

Part II (Prof.ssa Angela Parenti)

Progress will be assessed during the course, through interaction with the students and homeworks.

Capacità

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Skills

Part I (Prof. Paolo Frumento)

At the end of the course, students will achieve the ability to select an appropriate method to solve specific applied problems and to implement the necessary R code.

Part II (Prof.ssa Angela Parenti)

By the end of the course, students will know how to analyse the main challenges faced by economists and social scientists in answering empirical questions using micro‐data.

Modalità di verifica delle capacità

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Assessment criteria of skills

Part I (Prof. Paolo Frumento)

Students will be presented a number of projects in which they will be requested to answer practical questions using simulated data.

Part II (Prof.ssa Angela Parenti)

Some lectures will be devoted to empirical applications and will require the use of the

statistical software R. Many example will be carried out in order to understand how to use the right econometric specification and correctly interpret the empirical results.

Comportamenti

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Behaviors

Students will learn advanced R and advanced statistics, and their applications to relevant existing problems. This will enhanec their ability to collaborate with researchers from other fields.

It is expected that all students attend the lectures, be up to date with their readings, and

be prepared to participate fully in class.

Modalità di verifica dei comportamenti

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Assessment criteria of behaviors

During the lectures, the accuracy and precision of the activities carried out will be evaluated.

Prerequisiti (conoscenze iniziali)

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Prerequisites

Part I (Prof. Paolo Frumento)

The course will be advanced. To be able to attend, the student must have sufficient prior knowledge in probability, calculus, regression modeling, statistical inference.

Part II (Prof.ssa Angela Parenti)

Students need to be familiar with econometric theory at the level of Advanced Econometrics

(246PP) and the use of the R software.

Teaching methods

Lectures and lab sessions.

Programma (contenuti dell'insegnamento)

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Syllabus

**Part 1: Prof. Paolo Frumento**

- why statistical models: confounding, mediation, interaction, nonlinearity

- multiple linear regression (recap)

- logistic and Poisson regression

- methods for longitudinal data analysis (fixed effects, random effects models)

- quantile regression

- survival analysis (Kaplan-Meier curves, proportional hazards models, quantile regression)

- general theory of likelihood-based estimation and inference

The teacher is open to discuss additional topics, such as simulation, clustering, genetic algorithms, mixture models, generalized method of moments (GMM), asymptotic inference, generalized additive models (GAM).

**Part 2: Prof.ssa Angela Parenti**

*Causality*

- The problem of causality
- Rubin Causal Model: Potential outcomes model, treatment effects and selection bias
- Causality in a Regression Framework
- Instrumental Variables
- Difference-in-differences
- Regression Discontinuity Design

*Spatial econometrics*

- Introduction to spatial econometrics: motivating examples
- Spatial regression for estimating peer effects
- Spatial regression for estimating reaction functions
- Spatial panel

Bibliografia e materiale didattico

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Bibliography

Part I (Prof. Paolo Frumento)

Course material will be handed to the students. The following additional references are only recommended if interested in further reading:

- RB Millar (2011). Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB. Wiley, ISBN-13: 978-0470094822.

- D Kleinbaum and M klein (1996). Survival Analysis: A Self-Learning Text. Springer, ISBN: 978-1-4419-6646-9.

- R Koenker (2010). QUantile Regression. Cambridge University Press, online ISBN: 9780511754098.

- CCJH Bijleveld, LJT van der Kamp, A Mooijaart, WA van der Kloot, R van der Leeden, E van der Burg (1998). Longitudinal data analysis: Designs, models and methods. Sage Publications Ltd, ISBN-13: 978-0761955382.

Indicazioni per non frequentanti

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Non-attending students info

The same program and examination methods will be used for non-attending students.

Modalità d'esame

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Assessment methods

Part I (Prof. Paolo Frumento)

The exam will be entirely R-based. The students will be requested to answer specific questions using R.

Part II (Prof.ssa Angela Parenti)

The grade depends on a final exam, two homework assignments, and class

participation.

Updated: 11/12/2023 15:19

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