# Advanced Econometrics

**CORSO DI STUDIO: Corso di Laurea Magistrale in Economics-Scienze Economiche a.a. 2015/2016 **

## Denominazione insegnamento/Course Title

Advanced Econometrics

Econometria Avanzata

Lingua dell’insegnamento: Inglese

Crediti e ore di lezione: 6 CFU, 48-50 ore di lezione

Moduli: NO

Settore/i scientifico disciplinare: SECS-P/05

Docente: Laura Rizzi

Indirizzo email: laura.rizzi@uniud.it

Pagina web personale: http://people.uniud.it/page/laura.rizzi

## Prerequisiti e propedeuticità/Requirements

The course take place in the second period during the first year of the master in economics, then pre-requirements are not necessary.However, the knowledge of the subjects treated in the Econometrics course of the third year of the Economia e Commercio programme, together with basic statistical tools, are necessary to understand the arguments considered in the programme of advanced econometrics.

## Conoscenze e abilità da acquisire/Knowledge and skills

The course is aimed to deepen some applied econometrics methodologies generally usefull in the micro-economic contexts. Then tools used to handle micro data-bases with complexity issues will be treated during the lessons.

Ability relative to the discipline

The course is devoted to some econometrics models deepening, generally usefull in the analysis of economic and social phenomena chatacterized by both a micro and macro level and by a study design which requires the consideration of non linear or hierarchical models.

Some empirical examples in the economic and social context will be considered, together with the methodological aspects of Generalized Linear Models (GLM), Multilevel or Linear Mixed Models (LMM) and their combinations (GLMM).

At the end of the course each attending student need to be able to:

• Disentangle across the different forms of complexity in study designs related to empirical analysis;

• Apply the GLM models;

• Evaluate the estimation results derived from the GLMs’ application;

• Recognise the different forms of hierarchicy in the data and choose among the multilevel models able to study the roles of the levels of analysis;

• Estimate and evaluate the results obtained in the multilevel models estimation approach;

Soft skills:

• The econometric and statistical tools treated in the course will be usefull the empirical analysis on complex data-set which have to be performed in the following studies and researches required both in the following studies and in the working experiences;

• The student should be able to understand which is the best model to be applied on the basis of the reasearch interests;

• During the tutorial on PC the student will be endowed of the knowledge of the statistical softwares allowing the estimation of the trated models;

• Group projects, devoted to the analysis and presentation of scientific papers, each student will develop different abilities: the language ability to present the paper in english, the ability of comprehension of the analysed study both in its methodological and empirical part, the capacity to create a beamer presentation;

• This course treats tools which will be usefull also in other courses especially when a proper data analysis will be required.

## Programma e contenuti dell'insegnamento/Course description

After the course presentation and the description both of the ML estimation criterion and of the different forms of complexity in data-set, the course will focus:

1. Generalized Linear Model (GLM):

• Binary response variable models;

• Count response variable models;

• Models with categorical and ordinal response variable;

• Estimation and interpretation of results in empirical analysis with above models applied; Strutture di dati gerarchici e modelli multilivello (LMM):

2. Hierarchical structures in data-sets and their analysis’ opportunities;

• Multilevel models with random components and Linear Mixed Models (LMM);

• Estimation and interpretation of results in empirical analysis with above models applied;

3. Generalized Linear Mixed Models (GLMM):

• GLM models extensions to complex data structures;

• Estimation and interpretation of results in empirical analysis with GLMM models applied;

## Modalità di verifica dell'apprendimento/Examination

Examination for attending students is based on:

- Written exam at the end of the course which is characterised by 3 parts and both theoretical and empirical questions on estimated models results. These 3 parts are related to: GLM, multilevel models and GLMM. The written exam lasts 2 hours (compulsory).
- 2 group projects which consist on oral and written presentations of scientific papers on GLM and multilevel models;
- oral exam (not compulsory).

Examination for not attending students is based on:

- Written exam at the end of the course which is characterised by 4 parts and both theoretical and empirical questions on estimated models results. These 3 parts are related to: GLM, multilevel models and GLMM. The written exam lasts 2 hours (compulsory).
- oral exam (not compulsory).

The final score is obtained as the average of the scores relative to the written exam and the group projects. While the written exam is aimed to evaluate the theoretical and empirical knowledge attained during the course, the papers’ groups presentations are aimed to evaluate the students’ ability in comprehension and description of documents analysed using english language. Oral exam can be required by all students to increase the final score.

## Testi / Bibliografia/Bibliography

- Dobson, A.J.; Barnett, A.G. (2008).
*Introduction to Generalized Linear Models*(3rd ed.). Boca Raton, FL: Chapman and Hall/CRC. ISBN 1584881658. - McCullagh, Peter; Nelder, John (1989).
*Generalized Linear Models, Second Edition*. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5.

- Jiming Jiang. (2007) Linear and generalized linear mixed models and their applications (Springer Series in Statistics); ISBN-13: 9780387479415; ISBN: 0387479414

- Snijders, Tom AB and Bosker, Roel J, (2012). Multilevel Analysis, An Introduction to Basic and Advanced Multilevel Modeling. 2nd Edition.

Slides of the lessons (both theoretical and tutorials) are available in the University web page of the course. (“Materiale Didattico”).

## Strumenti a supporto della didattica/ Further readings and support material

Slides of the lessons (which will be available on the web page during the course period) are relative to the whole course programme. They represent the base material that need to be studied by each student. Other documents, relative to empirical studies applying GLM, LMM or GLMM, will be distributed to the attending students or made available on the web page and will represent the documents that groups of students will have to study and present. Lessons in classroom will be added by other ones in the PC room, devoted to the use of statistical softwares for the analysis of empirical data-sets.

## Tesi di laurea/Thesis

Even if this course is devoted to deepen tools which are usefull to the understanding of subjects treated in other courses of the master degree in Economics, the choice of this course as the basis for the final dissertation is possible. In fact it is possible to develop a dissertation characterized by the empirical application of the models treated in this course. It is preferable, however, to develop the dissertation within the context of the analysed econometric models.

## Note/Remarks

This course is aimed to endow the students with the ability of data analysis both in the social and in the economic context. The obtained skills are usefull in the empirical analysis adopted in other courses. The course programme is the same for attending and not attending students, however the latter ones do not participate to the group projects and their written exam will be added by a part with theoretical questions.