PhD in Economics and Management

Course partially running (all years except the first)

Training and Research

PhD Programme courses/classes PhD School courses/classes Faculty PhD students Calendar lesson PhD School Teaching Activities

PhD Programme Courses/classes - 2019/2020

Lezioni Dottorandi

Credits: 50

Language: Italian

Teacher: Valeria Franceschi, Catia Scricciolo

Behavioral and Experimental Economics

Credits: 5

Language: Italian

Teacher: Maria Vittoria Levati, Chiara Nardi, Luca Zarri

Corporate governance

Credits: 4

Language: Italian

Teacher: Alessandro Lai

Development Economics

Credits: 4

Language: Italian

Teacher: Federico Perali

Econometrics for management

Credits: 4

Language: Italian

Teacher: Francesca Rossi, Laura Magazzini

Energy Economics

Credits: 2,5

Language: Italian

Teacher: Luigi Grossi

Game Theory

Credits: 4

Language: Italian

Teacher: Francesco De Sinopoli

Inequality

Credits: 5

Language: Italian

Teacher: Francesco Andreoli, Claudio Zoli

Macro economics

Credits: 2,5

Language: Italian

Teacher: Alessia Campolmi

Macroeconomics I

Credits: 10

Language: Italian

Teacher: Claudio Zoli, Angelo Zago, Martina Menon

Mathematics

Credits: 7,5

Language: Italian

Teacher: Alberto Peretti, Athena Picarelli, Letizia Pellegrini

Organization Theory

Credits: 4

Language: Italian

Teacher: Cecilia Rossignoli, Alessandro Zardini, Lapo Mola

Political economy

Credits: 5

Language: Italian

Teacher: Emanuele Bracco, Roberto Ricciuti, Marcella Veronesi

Probability

Credits: 7,5

Language: Italian

Teacher: Marco Minozzo

Metodi quantitativi per la gestione aziendale

Credits: 5

Language: Italian

Teacher: Riccardo Scarpa

Supply Chain Management

Credits: 4

Language: Italian

Teacher: Barbara Gaudenzi

Teacher

Riccardo Scarpa

Referent

Riccardo Scarpa

Credits

Language

Italian

Class attendance

Free Choice

Location

VERONA

Moodle Seminars 0

Learning outcomes

1) Learn how to obtain help on the web to produce a technical document in LaTeX, from drafts to final submission format, according to journal specifications.
2) Learn how to develop a computational research plan that is fully reproducible and communicable to peers (based on R, Rmarkdown and Stata).
3) Learn how to comment and present code for future reference by oneself, use by collaborators and fellow students.
4) Comparing results across software, e.g. R versus Stata, and making sense of differences.
5) Learn advantages and disadvantages across computational tools.
6) Learn how to include figures, format tables, include bibliographical references dynamically in technical documents.

Program

1) Introduction to the most common LaTeX classes of documents for books, technical reports, and presentation slides.
2) Software for the management of bibliographies and automatic handling of citations and reference lists.
3) Introduction to the basic commands for R and principal packages for econometric analysis.
4) Introduction to the basic commands for Stata and principal packages for econometric analysis.
5) Introduction to Rmarkdown to compile quantitative documents dynamic documents in R e Stata.

Examination Methods

Students will be asked to produce assignments and will be assessed on the quality of these assignments.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE