Training and Research
PhD Programme Courses/classes
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
Statistica
Credits: 7.5
Language: Italian
Supply Chain Management
Credits: 4
Language: Italian
Teacher: Barbara Gaudenzi
Metodi quantitativi per la gestione aziendale (2019/2020)
Teacher
Referent
Credits
5
Language
Italian
Class attendance
Free Choice
Location
VERONA
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.
