Ateneo - UPO

The course will cover the following topics:

- introduction to Econometrics
- survey design;
- ordinary least square models
- binary choice models and models for categorical data;
- anchoring vignettes and hierarchical models;
- models and methods for panel data

Colin Cameron, Pravin Trivedi, Microeconometrics using STATA, STATA Press, 2009.

Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach (5th Edition), Cengage Learning, 2012.

Andrew M. Jones, Nigel Rice, Teresa Bago d'Uva, Silvia Balia; Applied Health Economics (2nd Edition); Taylor & Francis Ltd, 2012.

The course provides students with an understanding of the most relevant micro-econometric techniques.
The emphasis of the course is on applied work and on the illustration of the use of relevant computer software (STATA).

Statistics (probability and inference), Mathematics

The course is computer based. Relevant methods are presented alongside the Stata code and empirical results are discusses in class.

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

written exam (with the use of a PC)

The course will cover the following topics:

- introduction to Econometrics
- survey design;
- ordinary least square models
- binary choice models and models for categorical data;
- anchoring vignettes and hierarchical models;
- models and methods for panel data

By the end of the course, participants should be able to:
• formulate empirical problems involving micro-data
• select appropriate econometric methods
• understand methods of estimation and be able to implement them, using appropriate software
• construct usable datasets and know the limitations of the data
• interpret the results of the analysis