Training and Research
PhD Programme Courses/classes
Advice to Young Economists
Credits: 4
Language: English
Teacher: Marco Piovesan
Behavioral and Experimental Economics
Credits: 5
Language: Italian
Teacher: Simone Quercia, Maria Vittoria Levati, Marco Piovesan
Development Economics
Credits: 5
Language: English
Teacher: Federico Perali
Finance
Credits: 5
Language: English
Teacher: Cecilia Mancini
Game Theory
Credits: 5
Language: Inglese
Teacher: Francesco De Sinopoli
Inequality
Credits: 5
Language: English
Teacher: Francesco Andreoli, Claudio Zoli
Introduction to Probability – Module II (attività formativa per la Scuola di Dottorato)
Credits: 2
Language: Italian
Teacher: Claudia Di Caterina
Introduction to Probability – Module I
Credits: 2
Language: English
Introduction to Statistical Inference
Credits: 2
Language: English
Teacher: Marco Minozzo
Macroeconomics I
Credits: 7.5
Language: English
Teacher: Tamara Fioroni, Alessia Campolmi
Mathematics
Credits: 7.5
Language: English
Teacher: Letizia Pellegrini, Alberto Peretti
Microeconomics 1
Credits: 10.5
Language: English
Teacher: Simona Fiore, Claudio Zoli, Martina Menon
Political economy
Credits: 5
Language: English
Teacher: Emanuele Bracco, Roberto Ricciuti
Probability
Credits: 7.5
Language: English
Teacher: Marco Minozzo
Introduction to Statistical Inference (2022/2023)
Teacher
Referent
Credits
2
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
The purpose of the module is to explain, at an elementary level, the conceptual basis of the classical (frequentist) approach to statistical inference. The topics will be illustrated and explained through many examples.
Students are expected to acquire the language and the concepts needed to better understand the inferential procedures required for their subjects.
Prerequisites and basic notions
There are not particular learning requirements. It is advisable that students have already been introduced (though at an elementary level) to probability and statistics. It is also advisable that students have some confidence with elementary set theory and mathematical calculus.
Program
- Revision of limit theorems: weak law of large numbers; central limit theorem.
- Random samples, sample statistics and sampling distributions; normal and Bernoulli populations; sample mean, sample variance and sample proportion.
- Point estimation: estimators, unbiasedness, efficiency, mean square error, consistency.
- Interval estimation: pivotal quantity; paradigmatic examples.
- Hypothesis testing: type I and type II errors; critical value; confidence level; power; test statistic; observed significance level, paradigmatic examples.
Bibliography
Didactic methods
Lessons will be delivered via Zoom. Recordings will also be available. Presence to the lessons is not mandatory.
The calendar of the lessons is the following:
22 February 2023, 14:00-18:00
24 February 2023, 14:00-18:00
Learning assessment procedures
The final assessment will be through a written paper.
